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1
Vladimir Zlokolica,Lazar Velicki,Bojan Banjac,Marko Janev,Lidija Krstanovic,Nebojsa Ralevic,Ratko Obradovic,Bogoljub Mihajlovic
.2015;()1
[DOI]
2
Soulaimane Guedria,Noel De Palma,Felix Renard,Nicolas Vuillerme
.2019;()3330
[DOI]
3Deep learning enables automated volumetric assessments of cardiac function in zebrafish
Alexander A. Akerberg,Caroline E. Burns,C. Geoffrey Burns,Christopher Nguyen
Disease Models & Mechanisms.2019;()3330
[DOI]
4Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging
F Orologas,P Saitis,M Kallergi
Journal of Physics: Conference Series.2017;931()012039
[DOI]
5Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging
Jordan Fletcher,Danilo Miskovic
Journal of Physics: Conference Series.2021;931()95
[DOI]
6Development of a thresholding algorithm for calcium classification at multiple CT energies
LY. Ng,M. Alssabbagh,A. A. Tajuddin,I.L. Shuaib,R. Zainon
Journal of Physics: Conference Series.2017;851()012010
[DOI]
7Brain MR image segmentation using NAMS in pseudo-color
Hua Li,Chuanbo Chen,Shaohong Fang,Shengrong Zhao
Computer Assisted Surgery.2017;22(sup1)170
[DOI]
8Brain MR image segmentation using NAMS in pseudo-color
P. K. Bhagat,Prakash Choudhary
Computer Assisted Surgery.2019;698(sup1)543
[DOI]
9Brain MR image segmentation using NAMS in pseudo-color
Guohua Li
Computer Assisted Surgery.2016;698(sup1)379
[DOI]
10Automatic 3D segmentation of individual facial muscles using unlabeled prior information
Yousef Rezaeitabar,Ilkay Ulusoy
International Journal of Computer Assisted Radiology and Surgery.2012;7(1)35
[DOI]
11Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection
Lianfen Huang,Minghui Weng,Haitao Shuai,Yue Huang,Jianjun Sun,Fenglian Gao
BioMed Research International.2016;2016(1)1
[DOI]
12Obtaining pseudo-3D information from single-plane X-ray imaging
J. Hrdý,P. Oberta
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.2012;690(1)7
[DOI]
13A neuro-heuristic approach for recognition of lung diseases from X-ray images
Qiao Ke,Jiangshe Zhang,Wei Wei,Dawid Polap,Marcin Wozniak,Leon Kosmider,Robertas Damaševicius
Expert Systems with Applications.2019;126(1)218
[DOI]
14A neuro-heuristic approach for recognition of lung diseases from X-ray images
Preeti V. Joshi,C. D. Rawat
Expert Systems with Applications.2016;126(1)1212
[DOI]
15A neuro-heuristic approach for recognition of lung diseases from X-ray images
R. M. S. Ruth,L. Vanitha,F. F. Gnanaraj
Expert Systems with Applications.2012;126(1)699
[DOI]
16Review on 2D and 3D MRI Image Segmentation Techniques
S. Shirly,K. Ramesh
Current Medical Imaging Formerly Current Medical Imaging Reviews.2019;15(2)150
[DOI]
17Review on 2D and 3D MRI Image Segmentation Techniques
Santiago González Izard,Óscar Alonso Plaza,Ramiro Sánchez Torres,Juan Antonio Juanes Méndez,Francisco José García-Peñalvo
Current Medical Imaging Formerly Current Medical Imaging Reviews.2019;15(2)459
[DOI]
18Machine Learning-Enabled Smart Sensor Systems
Nam Ha,Kai Xu,Guanghui Ren,Arnan Mitchell,Jian Zhen Ou
Advanced Intelligent Systems.2020;2(9)2000063
[DOI]
19Image segmentation with arbitrary noise models by solving minimal surface problems
Daniel Tenbrinck,Xiaoyi Jiang
Pattern Recognition.2015;48(11)3293
[DOI]
20Image segmentation with arbitrary noise models by solving minimal surface problems
Robert J. Webster,Ziv R. Yaniv,Maggie Hess,Thomas Looi,Andras Lasso,Gabor Fichtinger,James Drake
Pattern Recognition.2015;9415(11)94152J
[DOI]
21Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases
Sarah Lindgren Belal,May Sadik,Reza Kaboteh,Olof Enqvist,Johannes Ulén,Mads H. Poulsen,Jane Simonsen,Poul F. Høilund-Carlsen,Lars Edenbrandt,Elin Trägårdh
European Journal of Radiology.2019;113(11)89
[DOI]
22Epistemic and aleatoric uncertainties reduction with rotation variation for medical image segmentation with ConvNets
Ge Zhang,Hao Dang,Yulong Xu
SN Applied Sciences.2022;4(2)89
[DOI]
23Phase-aberration compensation via deep learning in digital holographic microscopy
Shujun Ma,Rui Fang,Yu Luo,Qi Liu,Shiliang Wang,Xu Zhou
Measurement Science and Technology.2021;32(10)105203
[DOI]
24High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images
Luke A. Hunter,Shane Krafft,Francesco Stingo,Haesun Choi,Mary K. Martel,Stephen F. Kry,Laurence E. Court
Medical Physics.2013;40(12)121916
[DOI]
25Applications of Virtual and Augmented Reality in Biomedical Imaging
Santiago González Izard,Juan A. Juanes Méndez,Pablo Ruisoto Palomera,Francisco J. García-Peñalvo
Journal of Medical Systems.2019;43(4)121916
[DOI]
26Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots
Zheng Xu,Camilo Valdes,Jennifer Clarke
Agronomy.2018;8(5)71
[DOI]
27Three-dimensional printing and virtual surgery for congenital heart procedural planning
Ryan A. Moore,Kyle W. Riggs,Soultana Kourtidou,Kristen Schneider,Nicholas Szugye,Weston Troja,Gavin D'Souza,Mantosh Rattan,Roosevelt Bryant,Michael D. Taylor,David L.S. Morales
Birth Defects Research.2018;110(13)1082
[DOI]
28Three-dimensional printing and virtual surgery for congenital heart procedural planning
Mostafa Mohamed,Behrouz Far,Amr Guaily
Birth Defects Research.2012;110(13)220
[DOI]
29Object extraction from T2 weighted brain MR image using histogram based gradient calculation
Ghulam Gilanie,Muhammad Attique,Muhammad Hafeez-Ullah,Shahid Naweed,Ejaz Ahmed,Masroor Ikram
Pattern Recognition Letters.2013;34(12)1356
[DOI]
30Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine
K. Selva Bhuvaneswari,P. Geetha
Journal of Experimental & Theoretical Artificial Intelligence.2017;29(3)663
[DOI]
31Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality
Santiago González Izard,Ramiro Sánchez Torres,Óscar Alonso Plaza,Juan Antonio Juanes Méndez,Francisco José García-Peñalvo
Sensors.2020;20(10)2962
[DOI]
32Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality
Hossein Yousefi-Banaem,Saeed Kermani,Omid Sarrafzadeh,Davood Khodadad
Sensors.2013;20(10)1
[DOI]
33Quantitative morphometric analysis of adult teleost fish by X-ray computed tomography
Venera Weinhardt,Roman Shkarin,Tobias Wernet,Joachim Wittbrodt,Tilo Baumbach,Felix Loosli
Scientific Reports.2018;8(1)1
[DOI]
34Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Tariq Sadad,Asim Munir,Tanzila Saba,Ayyaz Hussain
Journal of Computational Science.2018;29(1)34
[DOI]
35Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Kumbham Bhargavi,Jangam J. S. Mani
Journal of Computational Science.2019;74(1)427
[DOI]
36Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Sourour Mesbahi,Hedi Yazid
Journal of Computational Science.2020;74(1)1
[DOI]
37Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Ionel-Bujorel Pavaloiu,Andrei Vasilateanu,Nicolae Goga,Iuliana Marin,Radu Ioanitescu,Alin-Anghel Dorobantu,Catalin Ilie,Marcel Blaga,Andrei Ungar,Ion Patrascu
Journal of Computational Science.2015;74(1)397
[DOI]
38Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Mao Li,Adam Wittek,Grand R. Joldes,Karol Miller
Journal of Computational Science.2016;74(1)85
[DOI]
39Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?
Maya Khalifé,Brice Fernandez,Olivier Jaubert,Michael Soussan,Vincent Brulon,Irène Buvat,Claude Comtat
Physics in Medicine & Biology.2017;62(19)7814
[DOI]
40AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation
Maria Baldeon-Calisto,Susana K. Lai-Yuen
Neurocomputing.2020;392(19)325
[DOI]
41Integrating AI into radiology workflow: levels of research, production, and feedback maturity
Engin Dikici,Matthew Bigelow,Luciano M. Prevedello,Richard D. White,Barbaros S. Erdal
Journal of Medical Imaging.2020;7(01)1
[DOI]
42Volumetric interpolation of tomographic sequences for accurate 3D reconstruction of anatomical parts
Chiara Santarelli,Fabrizio Argenti,Francesca Uccheddu,Luciano Alparone,Monica Carfagni
Computer Methods and Programs in Biomedicine.2020;194(01)105525
[DOI]
43Human cervical carcinoma detection and glucose monitoring in blood micro vasculatures with swept source OCT
H. Ullah,E. Ahmed,M. Ikram
JETP Letters.2013;97(12)690
[DOI]
44Mechanics informed fluoroscopy of esophageal transport
Sourav Halder,Shashank Acharya,Wenjun Kou,Peter J. Kahrilas,John E. Pandolfino,Neelesh A. Patankar
Biomechanics and Modeling in Mechanobiology.2021;20(3)925
[DOI]
45Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT
Sun-Young Park,Hong Il Ha,Sang Min Lee,In Jae Lee,Hyun Kyung Lim,Ewa Tomaszewska
PLOS ONE.2022;17(1)e0262025
[DOI]
46Multiparametric prostate MRI and structured reporting: benefits and challenges in the PI-RADS era
Sanas Mir-Bashiri,Kaneschka Yaqubi,Piotr Woznicki,Niklas Westhoff,Jost von Hardenberg,Thomas Huber,Matthias F. Froelich,Wieland H. Sommer,Maximilian F. Reiser,Stefan O. Schoenberg,Dominik Nörenberg
Chinese Journal of Academic Radiology.2021;4(1)21
[DOI]
47Multiparametric prostate MRI and structured reporting: benefits and challenges in the PI-RADS era
Suhuai Luo,Jesse J. Jin,Jiaming Li
Chinese Journal of Academic Radiology.2011;4(1)74
[DOI]
48Semiautomated spleen volumetry with diffusion-weighted MR imaging
Jeongjin Lee,Kyoung Won Kim,Ho Lee,So Jung Lee,Sanghyun Choi,Woo Kyoung Jeong,Heewon Kye,Gi-Won Song,Shin Hwang,Sung-Gyu Lee
Magnetic Resonance in Medicine.2012;68(1)305
[DOI]
49Semiautomated spleen volumetry with diffusion-weighted MR imaging
Toktam Khatibi,Mohammad Mehdi Sepehri,Mohammad Javad Soleimani,Pejman Shadpour
Magnetic Resonance in Medicine.2017;68(1)296
[DOI]
50Semiautomated spleen volumetry with diffusion-weighted MR imaging
Mingli Lu,Benlian Xu,Weijian Qin,Jian Shi
Magnetic Resonance in Medicine.2020;12145(1)215
[DOI]
51Semiautomated spleen volumetry with diffusion-weighted MR imaging
Mostafa Mohamed,Behrouz Far
Magnetic Resonance in Medicine.2012;12145(1)202
[DOI]
52Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography; CT myocardium segmentation
Bang Jun Guo,Xiuxiu He,Yang Lei,Joseph Harms,Tonghe Wang,Walter J. Curran,Tian Liu,Long Jiang Zhang,Xiaofeng Yang
Medical Physics.2020;47(4)1775
[DOI]
53A Systematic Review of the Techniques for the Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images
Malvika Ashok,Abhishek Gupta
Archives of Computational Methods in Engineering.2021;28(4)3245
[DOI]
54A statistical shape model of the human second cervical vertebra
Marine Clogenson,John M. Duff,Marcel Luethi,Marc Levivier,Reto Meuli,Charles Baur,Simon Henein
International Journal of Computer Assisted Radiology and Surgery.2015;10(7)1097
[DOI]
55Impacting clinical evaluation of anterior talofibular ligament injuries through analysis of ultrasound images
Vedpal Singh,Irraivan Elamvazuthi,Varun Jeoti,John George,Akshya Swain,Dileep Kumar
BioMedical Engineering OnLine.2016;15(1)1097
[DOI]
56Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs
Dimitrios Bounias,Ashish Singh,Spyridon Bakas,Sarthak Pati,Saima Rathore,Hamed Akbari,Michel Bilello,Benjamin A. Greenberger,Joseph Lombardo,Rhea D. Chitalia,Nariman Jahani,Aimilia Gastounioti,Michelle Hershman,Leonid Roshkovan,Sharyn I. Katz,Bardia Yousefi,Carolyn Lou,Amber L. Simpson,Richard K. G. Do,Russell T. Shinohara,Despina Kontos,Konstantina Nikita,Christos Davatzikos
Applied Sciences.2021;11(16)7488
[DOI]
57Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs
Abhi Lad,Swara Jani,Hiral Madhani,Soumya Soumya,Yash Solanki
Applied Sciences.2021;11(16)1
[DOI]
58A Survey of Graph Cuts/Graph Search Based Medical Image Segmentation
Xinjian Chen,Lingjiao Pan
IEEE Reviews in Biomedical Engineering.2018;11(16)112
[DOI]
59Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
Tahereh Hassanzadeh,Leonard G. C. Hamey,Kevin Ho-Shon
IEEE Access.2019;7(16)36748
[DOI]
60Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
R. R. Putri Amaristya Purwono,Endah Purwanti,Riries Rulaningtyas
IEEE Access.2020;2314(16)040009
[DOI]
61Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
Wei Xiong,Jierong Cheng,Ying Gu,Shimiao Li,Joo-Hwee Lim
IEEE Access.2015;2314(16)1
[DOI]
62Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
Ionel-Bujorel Pavaloiu,Andrei Vasilateanu,Nicolae Goga,Iuliana Marin,Catalin Ilie,Andrei Ungar,Ion Patrascu
IEEE Access.2014;2314(16)1
[DOI]
63Three-dimensional rotational angiography in congenital heart disease: Present status and evolving future
Sok-Leng Kang,Aimee Armstrong,Gregor Krings,Lee Benson
Congenital Heart Disease.2019;14(6)1046
[DOI]
64Digital Endocasting in Comparative Canine Brain Morphology
Kálmán Czeibert,Andrea Sommese,Örs Petneházy,Tibor Csörgo,Eniko Kubinyi
Frontiers in Veterinary Science.2020;7(6)1046
[DOI]
65Computational Patient Avatars for Surgery Planning
David González,Elías Cueto,Francisco Chinesta
Annals of Biomedical Engineering.2016;44(1)35
[DOI]
66A modified level set algorithm based on point distance shape constraint for lesion and organ segmentation
Xu Li,Chunming Li,Hairong Liu,Xiaoping Yang
Physica Medica.2019;57(1)123
[DOI]
67Dental hard tissue morphological segmentation with sparse representation-based classifier
Bin Cheng,Wei Wang
Medical & Biological Engineering & Computing.2019;57(8)1629
[DOI]
68Dental hard tissue morphological segmentation with sparse representation-based classifier
Rajaram Anantharaman,Matthew Velazquez,Yugyung Lee
Medical & Biological Engineering & Computing.2018;57(8)2197
[DOI]
69Dental hard tissue morphological segmentation with sparse representation-based classifier
Pranjal Pandey,Smita Pallavi,Subhash Chandra Pandey
Medical & Biological Engineering & Computing.2020;57(8)1
[DOI]
70Dental hard tissue morphological segmentation with sparse representation-based classifier
Jordan Fletcher,Danilo Miskovic
Medical & Biological Engineering & Computing.2021;57(8)45
[DOI]
71Dental hard tissue morphological segmentation with sparse representation-based classifier
Ionel-Bujorel Pavaloiu,Nicolae Goga,Iuliana Marin,Andrei Vasilateanu
Medical & Biological Engineering & Computing.2015;57(8)1
[DOI]
72Software Filtering Applications for the Analysis of Dental Images
Antoanela Naaji,Marius-Constantin Popescu,Gabriel Calin Sarla
WSEAS TRANSACTIONS ON SIGNAL PROCESSING.2020;16(8)59
[DOI]
73Gender Differences in Cerebral Regional Homogeneity of Adult Healthy Volunteers: A Resting-State fMRI Study
Chunsheng Xu,Chuanfu Li,Hongli Wu,Yuanyuan Wu,Sheng Hu,Yifang Zhu,Wei Zhang,Linying Wang,Senhua Zhu,Junping Liu,Qingping Zhang,Jun Yang,Xiaochu Zhang
BioMed Research International.2015;2015(8)1
[DOI]
74Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization
Arantza Bereciartua,Artzai Picon,Adrian Galdran,Pedro Iriondo
Biomedical Signal Processing and Control.2015;20(8)71
[DOI]
75Gastrointestinal duplication cysts: what a radiologist needs to know
Darshan Gandhi,Tushar Garg,Jignesh Shah,Harpreet Sawhney,Benjamin James Crowder,Arpit Nagar
Abdominal Radiology.2022;47(1)13
[DOI]
76Gastrointestinal duplication cysts: what a radiologist needs to know
Savitha Balakrishnan,Subashini Parthasarathy,Krishnaveni Marimuthu
Abdominal Radiology.2017;47(1)259
[DOI]
77Gastrointestinal duplication cysts: what a radiologist needs to know
B. P. Santosh Kumar,K. Venkata Ramanaiah
Abdominal Radiology.2020;643(1)325
[DOI]
78Gastrointestinal duplication cysts: what a radiologist needs to know
David Yee,Sara Soltaninejad,Deborsi Hazarika,Gaylord Mbuyi,Rishi Barnwal,Anup Basu
Abdominal Radiology.2017;643(1)216
[DOI]
79Compliance boundary conditions for patient-specific deformation simulation using the finite element method
Ece Ozkan,Orcun Goksel
Biomedical Physics & Engineering Express.2018;4(2)025003
[DOI]
80Hierarchy-NMS: Merging Candidate Bounding Boxes for Cerebrospinal Fluid Cell Image Segmentation
Xianwei Xu,Fangqi Li,Shilin Wang,Zhenhai Wang
Journal of Physics: Conference Series.2020;1693(1)012140
[DOI]
813D printing of cardiac structures from medical images: an overview of methods and interactive tools
Francesca Uccheddu,Monica Carfagni,Lapo Governi,Rocco Furferi,Yary Volpe,Erica Nocerino
International Journal on Interactive Design and Manufacturing (IJIDeM).2018;12(2)597
[DOI]
823D printing of cardiac structures from medical images: an overview of methods and interactive tools
Patrik Raudaschl,Karl Fritscher
International Journal on Interactive Design and Manufacturing (IJIDeM).2017;12(2)409
[DOI]
833D printing of cardiac structures from medical images: an overview of methods and interactive tools
Kapil Kumar Gupta,Namrata Dhanda,Upendra Kumar
International Journal on Interactive Design and Manufacturing (IJIDeM).2020;1064(2)197
[DOI]
84Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease
Zerwa Farooq,Ashkan Heshmatzadeh Behzadi,Jon D. Blumenfeld,Yize Zhao,Martin R. Prince
Clinical Imaging.2018;47(2)41
[DOI]
85Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease
Andrew M. Christensen,Katherine Weimer,Christopher Beaudreau,Michael Rensberger,Benjamin Johnson
Clinical Imaging.2018;47(2)23
[DOI]
86Segmentation algorithms for ear image data towards biomechanical studies
Ana Ferreira,Fernanda Gentil,João Manuel R. S. Tavares
Computer Methods in Biomechanics and Biomedical Engineering.2014;17(8)888
[DOI]
87Segmentation algorithms for ear image data towards biomechanical studies
Anusuya S. Venkatesan
Computer Methods in Biomechanics and Biomedical Engineering.2017;17(8)333
[DOI]
88Segmentation algorithms for ear image data towards biomechanical studies
Hajar Cherguif,Jamal Riffi,Mohamed Adnane Mahraz,Ali Yahyaouy,Hamid Tairi
Computer Methods in Biomechanics and Biomedical Engineering.2019;17(8)1
[DOI]
89Segmentation algorithms for ear image data towards biomechanical studies
M. M. A. Mohamed,B. Far
Computer Methods in Biomechanics and Biomedical Engineering.2012;17(8)947
[DOI]
90Segmentation algorithms for ear image data towards biomechanical studies
Asha Patil,Kalpesh Lad
Computer Methods in Biomechanics and Biomedical Engineering.2021;1187(8)223
[DOI]
91Towards a CAD-based automatic procedure for patient specific cutting guides to assist sternal osteotomies in pectus arcuatum surgical correction
Monica Carfagni,Flavio Facchini,Rocco Furferi,Marco Ghionzoli,Lapo Governi,Antonio Messineo,Francesca Uccheddu,Yary Volpe
Journal of Computational Design and Engineering.2019;6(1)118
[DOI]
92Adaptive thresholding technique based classification of red blood cell and sickle cell using Naïve Bayes Classifier and K-nearest neighbor classifier
Chayashree Patgiri,Amrita Ganguly
Biomedical Signal Processing and Control.2021;68(1)102745
[DOI]
93Automatic cell counting for phase-contrast microscopic images based on a combination of Otsu and watershed segmentation method
Yuefei Lin,Yong Diao,Yongzhao Du,Jianguang Zhang,Ling Li,Peizhong Liu
Microscopy Research and Technique.2022;85(1)169
[DOI]
94Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues
Muhammad Attique,Ghulam Gilanie,Ghulam Hafeez-Ullah,Malik S. Mehmood,Muhammad S. Naweed,Masroor Ikram,Javed A. Kamran,Alex Vitkin,Yu-Feng Zang
PLoS ONE.2012;7(3)e33616
[DOI]
95Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets
Adel Kermi,Khaled Andjouh,Ferhat Zidane
IET Image Processing.2018;12(11)1964
[DOI]
96Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study
Daniel F Polan,Samuel L Brady,Robert A Kaufman
Physics in Medicine and Biology.2016;61(17)6553
[DOI]
97What Do George Clooney and Sarah Jessica Parker Have in Common? Big-data
Toshimasa J. Clark,Rebecca J. Mieloszyk,Puneet Bhargava
Current Problems in Diagnostic Radiology.2017;46(3)171
[DOI]
98What Do George Clooney and Sarah Jessica Parker Have in Common? Big-data
Adel Kermi,Issam Mahmoudi,Mohamed Tarek Khadir
Current Problems in Diagnostic Radiology.2019;11384(3)37
[DOI]
99Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique
Jian-Wen Chen,Wan-Ju Lin,Chun-Yuan Lin,Che-Lun Hung,Chen-Pang Hou,Ching-Che Cho,Hong-Tsu Young,Chuan-Yi Tang
Applied Sciences.2020;10(14)4908
[DOI]
100Semi-automatic segmentation of whole-body images in longitudinal studies
Eloïse Grossiord,Laurent Risser,Salim Kanoun,Richard Aziza,Harold Chiron,Loïc Ysebaert,François Malgouyres,Soléakhéna Ken
Biomedical Physics & Engineering Express.2021;7(1)015014
[DOI]
101Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy
Doan Cong Le,Jirapa Chansangrat,Nattawut Keeratibharat,Paramate Horkaew
Diagnostics.2021;11(5)852
[DOI]
102Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy
Anusuya S. Venkatesan
Diagnostics.2018;11(5)1149
[DOI]
103Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy
Sweta Tripathi,R.S. Anand,E. Fernandez
Diagnostics.2018;11(5)1684
[DOI]
104Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy
Yao Chou,Dah Jye Lee,Dong Zhang
Diagnostics.2016;10072(5)628
[DOI]
105Spinal Cord MRI Segmentation Techniques and Algorithms: A Survey
Sheetal Garg,S. R. Bhagyashree
SN Computer Science.2021;2(3)628
[DOI]
106Development of an Endovascular Model of Pelvic Hemorrhage Using Volumetric Computed Tomography Validation
Hossam Abdou,Jonathan Du,Melike N. Harfouche,Neerav Patel,Joseph Edwards,Michael Richmond,Noha Elansary,Jonathan J. Morrison
Journal of Endovascular Therapy.2021;28(4)614
[DOI]
107Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis
Aaron Carass,Snehashis Roy,Adrian Gherman,Jacob C. Reinhold,Andrew Jesson,Tal Arbel,Oskar Maier,Heinz Handels,Mohsen Ghafoorian,Bram Platel,Ariel Birenbaum,Hayit Greenspan,Dzung L. Pham,Ciprian M. Crainiceanu,Peter A. Calabresi,Jerry L. Prince,William R. Gray Roncal,Russell T. Shinohara,Ipek Oguz
Scientific Reports.2020;10(1)614
[DOI]
108A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images
Mohammed S. Alqahtani,Mohamed Abbas,Ali Alqahtani,Mohammad Alshahrani,Abdulhadi Alkulib,Magbool Alelyani,Awad Almarhaby,Abdullah Alsabaani
Diagnostics.2021;11(5)855
[DOI]
109A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images
Mohammed S. Alqahtani,Mohamed Abbas,Ali Alqahtani,Mohammad Alshahrani,Abdulhadi Alkulib,Magbool Alelyani,Awad Almarhaby,Abdullah Alsabaani
Diagnostics.2016;11(5)1
[DOI]
110Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank
Matthew T MacLean,Qasim Jehangir,Marijana Vujkovic,Yi-An Ko,Harold Litt,Arijitt Borthakur,Hersh Sagreiya,Mark Rosen,David A Mankoff,Mitchell D Schnall,Haochang Shou,Julio Chirinos,Scott M Damrauer,Drew A Torigian,Rotonya Carr,Daniel J Rader,Walter R Witschey
Journal of the American Medical Informatics Association.2021;28(6)1178
[DOI]
111Artificial intelligence in medical imaging of the liver
Li-Qiang Zhou,Jia-Yu Wang,Song-Yuan Yu,Ge-Ge Wu,Qi Wei,You-Bin Deng,Xing-Long Wu,Xin-Wu Cui,Christoph F Dietrich
World Journal of Gastroenterology.2019;25(6)672
[DOI]
112Artificial intelligence in medical imaging of the liver
Fatemeh Zabihollahy,Eranga Ukwatta,Nicola Schieda
World Journal of Gastroenterology.2021;25(6)179
[DOI]
113Artificial intelligence in medical imaging of the liver
Amira S. Ashour,Nilanjan Dey
World Journal of Gastroenterology.2017;25(6)147
[DOI]
114From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications
Adam Wittek,Nicole M. Grosland,Grand Roman Joldes,Vincent Magnotta,Karol Miller
Annals of Biomedical Engineering.2016;44(1)3
[DOI]
115A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks
Ali Ibrahim Khaleel,Nik Adilah Hanin Zahri,Muhammad Imran Ahmad,Paolo Castaldo
Journal of Engineering.2021;2021(1)1
[DOI]
116A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks
Aleksandar Stojak,Eva Tuba,Milan Tuba
Journal of Engineering.2016;2021(1)1
[DOI]
117A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks
Peicheng Wu,Qing Chang
Journal of Engineering.2020;2021(1)635
[DOI]
118A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks
Shashwat Lal Das,John Keyser,Yoonsuck Choe
Journal of Engineering.2015;2021(1)1
[DOI]
119Pulling at the Digital Thread: Exploring the Tolerance Stack Up Between Automatic Procedures and Expert Strategies in Scan to Print Processes
Tobias Mahan,Nicholas Meisel,Christopher McComb,Jessica Menold
Journal of Mechanical Design.2019;141(2)1
[DOI]
120Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony
?.?. ?????,?.?. ??????
??????? ??????? ??????????.2021;141(1(164))104
[DOI]
121WDTISeg: One-Stage Interactive Segmentation for Breast Ultrasound Image Using Weighted Distance Transform and Shape-Aware Compound Loss
Xiaokang Li,Mengyun Qiao,Yi Guo,Jin Zhou,Shichong Zhou,Cai Chang,Yuanyuan Wang
Applied Sciences.2021;11(14)6279
[DOI]
122Development of a 3D breast shape generation and deformation system for breast implant fabrication
Gun-Yeol Na,Jeongsam Yang,Sungwoo Cho
Journal of Mechanical Science and Technology.2019;33(3)1293
[DOI]
123Development of a 3D breast shape generation and deformation system for breast implant fabrication
Ichrak Khoulqi,Najlae Idrissi
Journal of Mechanical Science and Technology.2020;33(3)384
[DOI]
124Magnetic resonance imaging of the pelvic floor: From clinical to biomechanical imaging
Sofia Brandão,Thuane Da Roza,Marco Parente,Isabel Ramos,Teresa Mascarenhas,Renato M Natal Jorge
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine.2013;227(12)1324
[DOI]
125A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient
Madhu Golla
International Journal of Computer Vision and Image Processing.2019;9(3)1
[DOI]
126A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient
Kalyani C.S.,Mallikarjuna Swamy M.S.
International Journal of Computer Vision and Image Processing.2016;9(3)34
[DOI]
127CT-based radiomics for differentiating renal tumours: a systematic review
Abhishta Bhandari,Muhammad Ibrahim,Chinmay Sharma,Rebecca Liong,Sonja Gustafson,Marita Prior
Abdominal Radiology.2021;46(5)2052
[DOI]
128Best methods and data to reconstruct paediatric lower limb bones for musculoskeletal modelling
Giorgio Davico,Claudio Pizzolato,Bryce A. Killen,Martina Barzan,Edin K. Suwarganda,David G. Lloyd,Christopher P. Carty
Biomechanics and Modeling in Mechanobiology.2020;19(4)1225
[DOI]
129Best methods and data to reconstruct paediatric lower limb bones for musculoskeletal modelling
Hsiao-Mei Chang,Ya-Yun Cheng,Hong-Ren Su,Shang-Hong Lai,Chu-Hsu Lin,Hung-Chih Hsu
Biomechanics and Modeling in Mechanobiology.2012;19(4)909
[DOI]
130Best methods and data to reconstruct paediatric lower limb bones for musculoskeletal modelling
H.C. DeSena,B.J. Landis,R.A. Moore,D.S. Spar,W. Whiteside,B.C. Blaxall
Biomechanics and Modeling in Mechanobiology.2017;19(4)361
[DOI]
131Development of multimodal neuroimaging markers for neurological disorders – Part 2
Kelvin K.L. Wong,Defeng Wang,Peipeng Liang,Kelvin Wong
Journal of X-Ray Science and Technology.2016;24(3)439
[DOI]
132Development of multimodal neuroimaging markers for neurological disorders – Part 2
Ahmed ElTanboly,Ali Mahmoud,Ahmed Shalaby,Magdi El-Azab,Mohammed Ghazal,Robert Keynton,Ayman El-Baz,Jasjit S. Suri
Journal of X-Ray Science and Technology.2019;24(3)207
[DOI]
133Voxel classification and graph cuts for automated segmentation of pathological periprosthetic hip anatomy
Daniel F. Malan,Charl P. Botha,Edward R. Valstar
International Journal of Computer Assisted Radiology and Surgery.2013;8(1)63
[DOI]
134Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image
Hyung Min Kim,Taehoon Ko,In Young Choi,Jun-Pyo Myong
International Journal of Medical Informatics.2022;158(1)104667
[DOI]
135Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks
Oscar Jimenez-del-Toro,Henning Muller,Markus Krenn,Katharina Gruenberg,Abdel Aziz Taha,Marianne Winterstein,Ivan Eggel,Antonio Foncubierta-Rodriguez,Orcun Goksel,Andras Jakab,Georgios Kontokotsios,Georg Langs,Bjoern H. Menze,Tomas Salas Fernandez,Roger Schaer,Anna Walleyo,Marc-Andre Weber,Yashin Dicente Cid,Tobias Gass,Mattias Heinrich,Fucang Jia,Fredrik Kahl,Razmig Kechichian,Dominic Mai,Assaf B. Spanier,Graham Vincent,Chunliang Wang,Daniel Wyeth,Allan Hanbury
IEEE Transactions on Medical Imaging.2016;35(11)2459
[DOI]
136Mathematical methods in biomedical imaging
Martin Burger,Jan Modersitzki,Daniel Tenbrinck
GAMM-Mitteilungen.2014;37(2)154
[DOI]
137A Semi-Automatic Algorithm for Determining the Demyelination Load in Metachromatic Leukodystrophy
Philipp Clas,Samuel Groeschel,Marko Wilke
Academic Radiology.2012;19(1)26
[DOI]
138A Comparison of Peripheral Imaging Technologies for Bone and Muscle Quantification: A Review of Segmentation Techniques
Andy Kin On Wong,Sarah Lynn Manske
Journal of Clinical Densitometry.2020;23(1)92
[DOI]
139Magician’s Corner: 4. Image Segmentation with U-Net
Bradley J. Erickson,Jason Cai
Radiology: Artificial Intelligence.2020;2(1)e190161
[DOI]
140Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm
Zhenhua Wang,Wenping Zhang,Yanan Sun,Mudi Yao,Biao Yan
BioMed Research International.2020;2020(1)1
[DOI]
141Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm
Maedeh Sadat Fasihi,Wasfy B. Mikhael
BioMed Research International.2016;2020(1)803
[DOI]
142Subject-specific rib finite element models with material data derived from coupon tests under bending loading
Keegan M. Yates,Amanda M. Agnew,Devon L. Albert,Andrew R. Kemper,Costin D. Untaroiu
Journal of the Mechanical Behavior of Biomedical Materials.2021;116(1)104358
[DOI]
143Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image
Manoj R. Tarambale,Nitin S. Lingayat
International Journal of Bioscience, Biochemistry and Bioinformatics.2013;116(1)545
[DOI]
144Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image
Vladimir Zlokolica,Lazar Velicki,Marko Janev,David Mitrinovic,Danilo Babin,Nebojsa Ralevic,Nada Cemerlic-Adic,Ratko Obradovic,Irena Galic
International Journal of Bioscience, Biochemistry and Bioinformatics.2014;116(1)1
[DOI]
145Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image
Haiyan Zheng,Yufei Chen,Xiaodong Yue,Chao Ma
International Journal of Bioscience, Biochemistry and Bioinformatics.2019;116(1)244
[DOI]
146Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image
Hong Zhu,Jinhui Xu,Junfeng Hu,Jing Chen
International Journal of Bioscience, Biochemistry and Bioinformatics.2017;10149(1)208
[DOI]
147Automated tooth segmentation as an innovative tool to assess 3D-tooth movement and root resorption in rodents
Viktoria Trelenberg-Stoll,Dieter Drescher,Michael Wolf,Kathrin Becker
Head & Face Medicine.2021;17(1)208
[DOI]
148Effect of reconstruction parameters on cone beam CT trabecular bone microstructure quantification in sheep
Aso Muhammad Ali Muhammad,Norliza Ibrahim,Rohana Ahmad,Muhammad Khan Asif,Zamri Radzi,Zuraiza Mohamad Zaini,Hairil Rashmizal Abdul Razak
BMC Oral Health.2020;20(1)208
[DOI]
149Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Kevin Arce,Jonathan M. Morris,Amy E. Alexander,Kyle S. Ettinger
Atlas of the Oral and Maxillofacial Surgery Clinics.2020;28(2)165
[DOI]
150Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Hayat Al-Dmour,Ahmed Al-Ani
Atlas of the Oral and Maxillofacial Surgery Clinics.2016;28(2)1
[DOI]
151Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Kelwin Fernandes,Jaime S. Cardoso
Atlas of the Oral and Maxillofacial Surgery Clinics.2018;28(2)1
[DOI]
152Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Emir Turajlic
Atlas of the Oral and Maxillofacial Surgery Clinics.2018;28(2)1104
[DOI]
153Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Emir Benson C. C.,Emir Lajish V. L.,Kumar Rajamani
Atlas of the Oral and Maxillofacial Surgery Clinics.2015;28(2)318
[DOI]
154Developing a Point-of-Care Manufacturing Program for Craniomaxillofacial Surgery
Muhammad Kaab Zarrar,Farhan Hussain,Muhammad Mohsin Khan,Rubab Sheikh
Atlas of the Oral and Maxillofacial Surgery Clinics.2019;28(2)1
[DOI]
155Segmentation of Three-Dimensional Images with Parametric Active Surfaces and Topology Changes
Heike Benninghoff,Harald Garcke
Journal of Scientific Computing.2017;72(3)1333
[DOI]
156Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients
Daniel Garzon-Chavez,Daniel Romero-Alvarez,Marco Bonifaz,Juan Gaviria,Daniel Mero,Narcisa Gunsha,Asiris Perez,María Garcia,Hugo Espejo,Franklin Espinosa,Edison Ligña,Mauricio Espinel,Emmanuelle Quentin,Enrique Teran,Francisco Mora,Jorge Reyes,Adriana Calderaro
PLOS ONE.2021;16(5)e0251295
[DOI]
157Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients
Hossam M. Moftah,Aboul Ella Hassanien,Adel M. Alimi,Hichem Karray,M.F. Tolba
PLOS ONE.2013;16(5)161
[DOI]
158Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients
Savitha Balakrishnan,Subashini Parthasarathy,Krishnaveni Marimuthu
PLOS ONE.2016;16(5)262
[DOI]
159Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients
Asim Waqas,Dimah Dera,Ghulam Rasool,Nidhal Carla Bouaynaya,Hassan M. Fathallah-Shaykh
PLOS ONE.2021;16(5)311
[DOI]
160A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package
Hongzhi Lan,Adam Updegrove,Nathan M. Wilson,Gabriel D. Maher,Shawn C. Shadden,Alison L. Marsden
Journal of Biomechanical Engineering.2018;140(2)311
[DOI]
161Reliability of 3D image analysis and influence of contrast medium administration on measurement of Hounsfield unit values of the proximal femur
Hye-Won Lee,Hong Il Ha,Sun-Young Park,Hyun Kyung Lim,Peter M.A. van Ooijen
PLOS ONE.2020;15(10)e0241012
[DOI]
162Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy
Jiayi Zhu,Bart Bolsterlee,Brian V. Y. Chow,Chengxue Cai,Robert D. Herbert,Yang Song,Erik Meijering
NMR in Biomedicine.2021;34(12)e0241012
[DOI]
163Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy
Yang Lei,Tonghe Wang,Yabo Fu,Justin Roper,Ashesh B. Jani,Tian Liu,Pretesh Patel,Xiaofeng Yang
Medical Physics.2021;48(11)7261
[DOI]
1643D printing: clinical applications in orthopaedics and traumatology
Ferdinando Auricchio,Stefania Marconi
EFORT Open Reviews.2016;1(5)121
[DOI]
165Augmented reality in gynecologic surgery: evaluation of potential benefits for myomectomy in an experimental uterine model
Nicolas Bourdel,Toby Collins,Daniel Pizarro,Adrien Bartoli,David Da Ines,Bruno Perreira,Michel Canis
Surgical Endoscopy.2017;31(1)456
[DOI]
166A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images
Mohamed T. Bennai,Zahia Guessoum,Smaine Mazouzi,Stéphane Cormier,Mohamed Mezghiche
Artificial Intelligence in Medicine.2020;110(1)101980
[DOI]
167Object-oriented classification approach for bone metastasis mapping from whole-body bone scintigraphy
Mihaela Antonina Calin,Florina-Gianina Elfarra,Sorin Viorel Parasca
Physica Medica.2021;84(1)141
[DOI]
168A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT
Yuan Feng,Iwan Kawrakow,Jeff Olsen,Parag J. Parikh,Camille Noel,Omar Wooten,Dongsu Du,Sasa Mutic,Yanle Hu
Journal of Applied Clinical Medical Physics.2016;17(2)441
[DOI]
169A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT
Sanjay Saxena,Adhesh Garg,Puspanjali Mohapatra
Journal of Applied Clinical Medical Physics.2019;17(2)153
[DOI]
170Dendritic tree extraction from noisy maximum intensity projection images in C. elegans
Ayala Greenblum,Raphael Sznitman,Pascal Fua,Paulo E Arratia,Meital Oren,Benjamin Podbilewicz,Josué Sznitman
BioMedical Engineering OnLine.2014;13(1)153
[DOI]
171Dendritic tree extraction from noisy maximum intensity projection images in C. elegans
Vitaly Boitsov,Roman Soldatov,Rajdeep Niyogi,Alexandra Vatian,Nikolay Egorov,Anton Klochkov,Artem Lobantsev,Ekaterina Markova,Natalia Gusarova,Anatoly Shalyto,Alexey Zubanenko
BioMedical Engineering OnLine.2019;13(1)64
[DOI]
172Dendritic tree extraction from noisy maximum intensity projection images in C. elegans
M D Nandeesh,M Meenakshi
BioMedical Engineering OnLine.2019;13(1)1419
[DOI]
173Individualized 3D-printed templates for high-dose-rate interstitial multicathether brachytherapy in patients with breast cancer
Cynthia Aristei,Valentina Lancellotta,Marco Piergentini,Giacomo Costantini,Simonetta Saldi,Sara Chierchini,Antonella Cavalli,Luca Di Renzo,Oscar Fiorucci,Massimo Guasticchi,Vittorio Bini,Alessandro Ricci
Brachytherapy.2019;18(1)57
[DOI]
174Individualized 3D-printed templates for high-dose-rate interstitial multicathether brachytherapy in patients with breast cancer
B. S. Maya,T. Asha
Brachytherapy.2018;25(1)85
[DOI]
175Three-Dimensional Reconstruction Volume: A Novel Method for Volume Measurement in Kidney Cancer
Timothy A. Durso,Jonathan Carnell,Thomas T. Turk,Gopal N. Gupta
Journal of Endourology.2014;28(6)745
[DOI]
176Three-Dimensional Reconstruction Volume: A Novel Method for Volume Measurement in Kidney Cancer
Tiago Gonçalves,Wilson Silva,Jaime Cardoso
Journal of Endourology.2020;76(6)1967
[DOI]
177Micro-CT scan with virtual dissection of left ventricle is a non-destructive, reproducible alternative to dissection and weighing for left ventricular size
Ata Doost,Alejandra Rangel,Quang Nguyen,Grant Morahan,Leonard Arnolda
Scientific Reports.2020;10(1)1967
[DOI]
178Micro-CT scan with virtual dissection of left ventricle is a non-destructive, reproducible alternative to dissection and weighing for left ventricular size
Martin Tamajka,Wanda Benesova
Scientific Reports.2016;10(1)1
[DOI]
179A software tool for studying the size and shape of human cardiomyocytes
Jyrki Rasku,Marisa Ojala,Risto-Pekka Pölönen,Henry Joutsijoki,Yulia Gizatdinova,Jorma Laurikkala,Kimmo Kartasalo,Katriina Aalto-Setälä,Martti Juhola
Biomedical Signal Processing and Control.2016;30(1)134
[DOI]
180A software tool for studying the size and shape of human cardiomyocytes
Alexander Zotin,Konstantin Simonov,Fedor Kapsargin,Tatyana Cherepanova,Alexey Kruglyakov,Luis Cadena
Biomedical Signal Processing and Control.2018;136(1)223
[DOI]
181A software tool for studying the size and shape of human cardiomyocytes
Nursuriati Jamil,Hazwani Che Soh,Tengku Mohd Tengku Sembok,Zainab Abu Bakar
Biomedical Signal Processing and Control.2011;7066(1)99
[DOI]
182SCAU-Net: Spatial-Channel Attention U-Net for Gland Segmentation
Peng Zhao,Jindi Zhang,Weijia Fang,Shuiguang Deng
Frontiers in Bioengineering and Biotechnology.2020;8(1)99
[DOI]
183SCAU-Net: Spatial-Channel Attention U-Net for Gland Segmentation
Faiza Bukenya,Amir Awwad,Jinming Duan,Josef Ehling,Henryk Faas,Li Bai
Frontiers in Bioengineering and Biotechnology.2018;8(1)1508
[DOI]
184Binary segmentation of medical images using implicit spline representations and deep learning
Oliver J.D. Barrowclough,Georg Muntingh,Varatharajan Nainamalai,Ivar Stangeby
Computer Aided Geometric Design.2021;85(1)101972
[DOI]
185Binary segmentation of medical images using implicit spline representations and deep learning
Oliver J.D. Barrowclough,Georg Muntingh,Varatharajan Nainamalai,Ivar Stangeby
Computer Aided Geometric Design.2016;85(1)249
[DOI]
186Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery
Tonghe Wang,Yang Lei,Sibo Tian,Xiaojun Jiang,Jun Zhou,Tian Liu,Sean Dresser,Walter J. Curran,Hui-Kuo Shu,Xiaofeng Yang
Medical Physics.2019;46(7)3133
[DOI]
187Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery
J. E. Anusha Linda Kostka
Medical Physics.2019;75(7)285
[DOI]
188Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery
Abanti Shama Afroz
Medical Physics.2014;75(7)1
[DOI]
189Brain tumor segmentation using DE embedded OTSU method and neural network
Anshika Sharma,Sushil Kumar,Shailendra Narayan Singh
Multidimensional Systems and Signal Processing.2019;30(3)1263
[DOI]
190Automated Segmentation of Tissues Using CT and MRI: A Systematic Review
Leon Lenchik,Laura Heacock,Ashley A. Weaver,Robert D. Boutin,Tessa S. Cook,Jason Itri,Christopher G. Filippi,Rao P. Gullapalli,James Lee,Marianna Zagurovskaya,Tara Retson,Kendra Godwin,Joey Nicholson,Ponnada A. Narayana
Academic Radiology.2019;26(12)1695
[DOI]
191Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy
Yabo Fu,Yang Lei,Tonghe Wang,Sibo Tian,Pretesh Patel,Ashesh B. Jani,Walter J. Curran,Tian Liu,Xiaofeng Yang
Medical Physics.2020;47(8)3415
[DOI]
192Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy
Michel M. dos Santos,Mêuser J. S. Valença,Wellington P. dos Santos
Medical Physics.2012;7435(8)135
[DOI]
193The role of AI technology in prediction, diagnosis and treatment of colorectal cancer
Chaoran Yu,Ernest Johann Helwig
Artificial Intelligence Review.2021;7435(8)135
[DOI]
194Sparse regularization for EIT reconstruction incorporating structural information derived from medical imaging
Bo Gong,Benjamin Schullcke,Sabine Krueger-Ziolek,Ullrich Mueller-Lisse,Knut Moeller
Physiological Measurement.2016;37(6)843
[DOI]
195Watertight 2-manifold 3D bone surface model reconstruction from CT images based on visual hyper-spherical mapping
Tianran Yuan,Hongsheng Zhang,Hao Liu,Juan Du,Huiming Yu,Yimin Wang,Yabin Xu
Mathematical Biosciences and Engineering.2021;18(2)1280
[DOI]
196Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning
Samaneh Kazemifar,Anjali Balagopal,Dan Nguyen,Sarah McGuire,Raquibul Hannan,Steve Jiang,Amir Owrangi
Biomedical Physics & Engineering Express.2018;4(5)055003
[DOI]
197Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning
Sk. Hasane Ahammad,Md. Zia Ur Rahman,A. Lay-Ekuakille,N. I. Giannoccaro
Biomedical Physics & Engineering Express.2020;4(5)1
[DOI]
198Auto-delineation framework for the focal liver reaction observed in post Stereotactic Body Radiation Therapy (SBRT) Primovist MRI scans
Svetlana Kuznetsova,Petra Grendarova,Rishi Sinha,Nicolas Ploquin,Kundan Thind
Medical Dosimetry.2021;4(5)1
[DOI]
199Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates
Alžbeta Turecková,Tomáš Turecek,Zuzana Komínková Oplatková,Antonio Rodríguez-Sánchez
Frontiers in Robotics and AI.2020;7(5)1
[DOI]
200SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES
Gaurav Sethi,B. S. Saini
Biomedical Engineering: Applications, Basis and Communications.2015;27(05)1550047
[DOI]
201An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
Fahmi Akmal Dzulkifli,Mohd Yusoff Mashor,Hasnan Jaafar
Journal of Physics: Conference Series.2019;1372(1)012060
[DOI]
202An Automated Segmentation and Counting of Ki67 Cells in Meningioma Using K-Means Clustering Technique
Yenatfanta Shifferaw,Kumudha Raimond
Journal of Physics: Conference Series.2015;1372(1)41
[DOI]
203Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique
Ahmed Kharrat,Karim Gasmi,Mohamed Ben Messaoud,Nacéra Benamrane,Mohamed Abid
International Journal of Software Science and Computational Intelligence.2011;3(2)19
[DOI]
204Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique
Igor Ruban,Hennadii Khudov
International Journal of Software Science and Computational Intelligence.2020;876(2)53
[DOI]
205A tool for precise calculation of organ doses in voxelised geometries using GAMOS/Geant4 with a graphical user interface
Pedro Arce Dubois,Nguyen Thi Phuong Thao,Nguyen Thien Trung,Juan Diego Azcona,Pedro-Borja Aguilar-Redondo
Polish Journal of Medical Physics and Engineering.2021;27(1)31
[DOI]
206Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
Li Sun,Songtao Zhang,Hang Chen,Lin Luo
Frontiers in Neuroscience.2019;13(1)31
[DOI]
207Comparison of two methods for the estimation of subcortical volume and asymmetry using magnetic resonance imaging: a methodological study
Tolga Ertekin,Niyazi Acer,Semra Içer,Ahmet T. Ilica
Surgical and Radiologic Anatomy.2013;35(4)301
[DOI]
208Differences in left ventricular measurements: Attenuation versus contour based methods
Yici Liu,Sophia Bourgeois,Yeung Yam,Gary R. Small,Benjamin J.W. Chow
Journal of Cardiovascular Computed Tomography.2019;13(4)174
[DOI]
209Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Inês Domingues,Gisèle Pereira,Pedro Martins,Hugo Duarte,João Santos,Pedro Henriques Abreu
Artificial Intelligence Review.2020;53(6)4093
[DOI]
210Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Nadim Mahmud Dipu,Sifatul Alam Shohan,K. M. A. Salam
Artificial Intelligence Review.2021;53(6)1
[DOI]
211Deep learning for the fully automated segmentation of the inner ear on MRI
Akshayaa Vaidyanathan,Marly F. J. A. van der Lubbe,Ralph T. H. Leijenaar,Marc van Hoof,Fadila Zerka,Benjamin Miraglio,Sergey Primakov,Alida A. Postma,Tjasse D. Bruintjes,Monique A. L. Bilderbeek,Hammer Sebastiaan,Patrick F. M. Dammeijer,Vincent van Rompaey,Henry C. Woodruff,Wim Vos,Seán Walsh,Raymond van de Berg,Philippe Lambin
Scientific Reports.2021;11(1)1
[DOI]
212Liver segmentation: indications, techniques and future directions
Akshat Gotra,Lojan Sivakumaran,Gabriel Chartrand,Kim-Nhien Vu,Franck Vandenbroucke-Menu,Claude Kauffmann,Samuel Kadoury,Benoît Gallix,Jacques A. de Guise,An Tang
Insights into Imaging.2017;8(4)377
[DOI]
213Liver segmentation: indications, techniques and future directions
S Sudha,K B Jayanthi,C Rajasekaran,T Sunder
Insights into Imaging.2019;8(4)767
[DOI]
214SUSAN: segment unannotated image structure using adversarial network
Fang Liu
Magnetic Resonance in Medicine.2018;8(4)767
[DOI]
2153D active surfaces for liver segmentation in multisequence MRI images
Arantza Bereciartua,Artzai Picon,Adrian Galdran,Pedro Iriondo
Computer Methods and Programs in Biomedicine.2016;132(4)149
[DOI]
216Kidney and Tumor Segmentation using U-Net Deep Learning Model
Rochan Sharma,Pallavi Halarnkar,Kiran Choudhari
SSRN Electronic Journal.2020;132(4)149
[DOI]
217Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps
Kristoffer Wickstrøm,Michael Kampffmeyer,Robert Jenssen
Medical Image Analysis.2020;60(4)101619
[DOI]
218Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy
R Sungura,E Mpolya,JM Spitsbergen,C Onyambu,E Sauli,J-M Vianney
European Journal of Radiology Open.2020;7(4)100281
[DOI]
219Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method
Gaurav Sethi,B.S. Saini,Dilbag Singh
Computers & Electrical Engineering.2016;53(4)244
[DOI]
220Towards Non-Invasive Methods to Assess Population Structure and Biomass in Vulnerable Sea Pen Fields
Giovanni Chimienti,Attilio Di Nisio,Anna M. L. Lanzolla,Gregorio Andria,Angelo Tursi,Francesco Mastrototaro
Sensors.2019;19(10)2255
[DOI]
221Towards Non-Invasive Methods to Assess Population Structure and Biomass in Vulnerable Sea Pen Fields
B. P. Santosh Kumar,K. V. Ramanaiah
Sensors.2019;19(10)202
[DOI]
222Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy
Xianjin Dai,Yang Lei,Tonghe Wang,Anees H Dhabaan,Mark McDonald,Jonathan J Beitler,Walter J Curran,Jun Zhou,Tian Liu,Xiaofeng Yang
Physics in Medicine & Biology.2021;66(4)045021
[DOI]
223Level set approach based on Parzen Window and floor of log for edge computing object segmentation in digital images
Elizângela de Souza Rebouças,Fátima Nelsizeuma Sombra de Medeiros,Regis Cristiano P. Marques,João Victor S. Chagas,Matheus T. Guimarães,Lucas O. Santos,Aldisio G. Medeiros,Solon A. Peixoto
Applied Soft Computing.2021;105(4)107273
[DOI]
224Level set approach based on Parzen Window and floor of log for edge computing object segmentation in digital images
Kapil Kumar Gupta,Namrata Dhanda,Upendra Kumar
Applied Soft Computing.2018;105(4)1
[DOI]
225Unveiling buried aeolian landscapes: reconstructing a late Holocene dune environment using 3D ground-penetrating radar
Luis Rees-Hughes,Natasha L. M. Barlow,Adam D. Booth,Landis J. West,George Tuckwell,Tim Grossey
Journal of Quaternary Science.2021;36(3)377
[DOI]
226Unveiling buried aeolian landscapes: reconstructing a late Holocene dune environment using 3D ground-penetrating radar
A. Sindhu,V. Radha
Journal of Quaternary Science.2020;1108(3)514
[DOI]
227Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images
Geet Lahoti,Jialei Chen,Xiaowei Yue,Hao Yan,Chitta Ranjan,Zhen Qian,Chuck Zhang,Ben Wang
IISE Transactions on Healthcare Systems Engineering.2021;1108(3)1
[DOI]
228Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images
Deepthy Mary Alex,D. Abraham Chandy
IISE Transactions on Healthcare Systems Engineering.2020;332(3)185
[DOI]
229Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images
Hiren Mewada,Suprava Patnaik
IISE Transactions on Healthcare Systems Engineering.2014;332(3)1
[DOI]
230Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images
Rupal R. Agravat,Mehul S. Raval
IISE Transactions on Healthcare Systems Engineering.2018;332(3)183
[DOI]
231Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images
Saulo Vargas,Mauricio Edgar Stivanello,Mario Lucio Roloff,Juliano Emir Nunes Masson,Ederson Stiegelmaier
IISE Transactions on Healthcare Systems Engineering.2016;332(3)1
[DOI]
232Crowdsourcing human-based computation for medical image analysis: A systematic literature review
Nataša Petrovic,Gabriel Moyà-Alcover,Javier Varona,Antoni Jaume-i-Capó
Health Informatics Journal.2020;26(4)2446
[DOI]
233Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare
Tariq Sadad,Ayyaz Hussain,Asim Munir,Muhammad Habib,Sajid Ali Khan,Shariq Hussain,Shunkun Yang,Mohammed Alawairdhi
Applied Sciences.2020;10(6)1900
[DOI]
234DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang,Maria A. Zuluaga,Wenqi Li,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren
IEEE Transactions on Pattern Analysis and Machine Intelligence.2019;41(7)1559
[DOI]
235DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Daniel Bell,Candace Moore
IEEE Transactions on Pattern Analysis and Machine Intelligence.2019;41(7)1559
[DOI]
236Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning
L. Umapathy,B. Winegar,L. MacKinnon,M. Hill,M.I. Altbach,J.M. Miller,A. Bilgin
American Journal of Neuroradiology.2020;41(6)1061
[DOI]
237Designing of Ground-Truth-Annotated DBT-TU-JU Breast Thermogram Database Toward Early Abnormality Prediction
Mrinal Kanti Bhowmik,Usha Rani Gogoi,Gautam Majumdar,Debotosh Bhattacharjee,Dhritiman Datta,Anjan Kumar Ghosh
IEEE Journal of Biomedical and Health Informatics.2018;22(4)1238
[DOI]
238Dynamics of hydrogen loss and structural changes in pyrolyzing biomass utilizing neutron imaging
Frederik Ossler,Charles E.A. Finney,Jeffrey M. Warren,Jean-Christophe Bilheux,Yuxuan Zhang,Rebecca A. Mills,Louis J. Santodonato,Hassina Z. Bilheux
Carbon.2021;176(4)511
[DOI]
239Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach
Yesenia Gonzalez,Chenyang Shen,Hyunuk Jung,Dan Nguyen,Steve B. Jiang,Kevin Albuquerque,Xun Jia
Medical Image Analysis.2021;68(4)101896
[DOI]
240Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach
Vartika Agrawal,Satish Chandra
Medical Image Analysis.2015;68(4)171
[DOI]
241Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach
Nadia Magnenat Thalmann,Hon Fai Choi,Daniel Thalmann
Medical Image Analysis.2014;68(4)3
[DOI]
242Skin lesion segmentation using fully convolutional networks: A comparative experimental study
Ruya Kaymak,Cagri Kaymak,Aysegul Ucar
Expert Systems with Applications.2020;161(4)113742
[DOI]
243On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey
Ane Alberdi,Asier Aztiria,Adrian Basarab
Artificial Intelligence in Medicine.2016;71(4)1
[DOI]
244Photodynamic therapy of deep tissue abscess cavities: Retrospective image-based feasibility study using Monte Carlo simulation
Timothy M. Baran,Hyun W. Choi,Mattison J. Flakus,Ashwani K. Sharma
Medical Physics.2019;46(7)3259
[DOI]
245Photodynamic therapy of deep tissue abscess cavities: Retrospective image-based feasibility study using Monte Carlo simulation
Avik Hati,Matteo Bustreo,Diego Sona,Vittorio Murino,Alessio Del Bue
Medical Physics.2021;46(7)5565
[DOI]
246Biomechanical model for computing deformations for whole-body image registration: A meshless approach
Mao Li,Karol Miller,Grand Roman Joldes,Ron Kikinis,Adam Wittek
International Journal for Numerical Methods in Biomedical Engineering.2016;32(12)5565
[DOI]
247Biomechanical model for computing deformations for whole-body image registration: A meshless approach
Hassan Mahmoud,Francesco Masulli,Stefano Rovetta
International Journal for Numerical Methods in Biomedical Engineering.2013;7845(12)37
[DOI]
248Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients
Elanchezhian Somasundaram,Joanna Deaton,Robert Kaufman,Samuel Brady
Physics in Medicine & Biology.2018;63(13)135009
[DOI]
249Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients
Kishore Babu Nampalle,Balasubramanian Raman
Physics in Medicine & Biology.2021;1376(13)430
[DOI]
250Image Segmentation Based on Modified Fractional Allen–Cahn Equation
Dongsun Lee,Seunggyu Lee
Mathematical Problems in Engineering.2019;2019(13)1
[DOI]
251Automated Breast Density Measurements From Chest Computed Tomography Scans
Touseef A. Qureshi,Harini Veeraraghavan,Janice S. Sung,Jennifer B. Kaplan,Jessica Flynn,Emily S. Tonorezos,Suzanne L. Wolden,Elizabeth A. Morris,Kevin C. Oeffinger,Malcolm C. Pike,Chaya S. Moskowitz
Journal of Medical Systems.2019;43(8)1
[DOI]
252Validated reconstructions of geometries of nasal cavities from CT scans
David Zwicker,Kai Yang,Simone Melchionna,Michael P Brenner,Bob Liu,Robin W Lindsay
Biomedical Physics & Engineering Express.2018;4(4)045022
[DOI]
253R2D2: A scalable deep learning toolkit for medical imaging segmentation
Soulaimane Guedria,Noël De Palma,Félix Renard,Nicolas Vuillerme
Software: Practice and Experience.2020;50(10)1966
[DOI]
254R2D2: A scalable deep learning toolkit for medical imaging segmentation
Javeria Umer,Aun Irtaza,Nudrat Nida
Software: Practice and Experience.2020;50(10)1
[DOI]
255Accuracy of joint line restoration based on three-dimensional registration of the contralateral tibial tuberosity and the fibular tip
Sandro Hodel,Anna-Katharina Calek,Philipp Fürnstahl,Sandro F. Fucentese,Lazaros Vlachopoulos
Journal of Experimental Orthopaedics.2021;8(1)1
[DOI]
256Accuracy of joint line restoration based on three-dimensional registration of the contralateral tibial tuberosity and the fibular tip
Sharmili Roy,Michael S. Brown,George L. Shih
Journal of Experimental Orthopaedics.2012;8(1)725
[DOI]
257Radiogenomics in Colorectal Cancer
Bogdan Badic,Florent Tixier,Catherine Cheze Le Rest,Mathieu Hatt,Dimitris Visvikis
Cancers.2021;13(5)973
[DOI]
258Recent computational methods for white blood cell nuclei segmentation: A comparative study
Alan R. Andrade,Luis H.S. Vogado,Rodrigo de M.S. Veras,Romuere R.V. Silva,Flávio H.D. Araujo,Fátima N.S. Medeiros
Computer Methods and Programs in Biomedicine.2019;173(5)1
[DOI]
259Recent computational methods for white blood cell nuclei segmentation: A comparative study
Tolulope Bamidele Ijitona,Jinchang Ren,Phil Byongjun Hwang
Computer Methods and Programs in Biomedicine.2014;173(5)168
[DOI]
260Tonguenet: Accurate Localization and Segmentation for Tongue Images Using Deep Neural Networks
Changen Zhou,Haoyi Fan,Zuoyong Li
IEEE Access.2019;7(5)148779
[DOI]
261Adaptive chemical reaction based spatial fuzzy clustering for level set segmentation of medical images
V. Asanambigai,J. Sasikala
Ain Shams Engineering Journal.2018;9(4)1251
[DOI]
262Artificial intelligence in radiology
Ahmed Hosny,Chintan Parmar,John Quackenbush,Lawrence H. Schwartz,Hugo J. W. L. Aerts
Nature Reviews Cancer.2018;18(8)500
[DOI]
263Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis
Laszlo Papp,Clemens P. Spielvogel,Ivo Rausch,Marcus Hacker,Thomas Beyer
Frontiers in Physics.2018;6(8)500
[DOI]
264Automated cerebellar segmentation: Validation and application to detect smaller volumes in children prenatally exposed to alcohol
Valerie A. Cardenas,Mathew Price,M. Alejandra Infante,Eileen M. Moore,Sarah N. Mattson,Edward P. Riley,George Fein
NeuroImage: Clinical.2014;4(8)295
[DOI]
265Automated cerebellar segmentation: Validation and application to detect smaller volumes in children prenatally exposed to alcohol
Tianyu Ma,Hang Zhang,Hanley Ong,Amar Vora,Thanh D. Nguyen,Ajay Gupta,Yi Wang,Mert R. Sabuncu
NeuroImage: Clinical.2021;4(8)325
[DOI]
266Development of an automated multi-thresholding technique for identification of different materials types and concentration using CT scans
et al. Ng
International Journal of ADVANCED AND APPLIED SCIENCES.2018;5(3)1
[DOI]
267Photonic approach for simultaneous measurement of microwave DFS and AOA
Jianing Zhao,Zhenzhou Tang,Shilong Pan
Applied Optics.2021;60(16)4622
[DOI]
268Photonic approach for simultaneous measurement of microwave DFS and AOA
Faiza Bukenya,Josef Ehling,Abdu Kiweewa Kalema,Imo Eyoh,John Robert,Li Bai
Applied Optics.2016;60(16)1
[DOI]
269Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang,Wenqi Li,Michael Aertsen,Jan Deprest,Sébastien Ourselin,Tom Vercauteren
Neurocomputing.2019;338(16)34
[DOI]
270Micro-computed tomography imaging and analysis in developmental biology and toxicology
L. David Wise,Christopher T. Winkelmann,Belma Dogdas,Ansuman Bagchi
Birth Defects Research Part C: Embryo Today: Reviews.2013;99(2)71
[DOI]
271Micro-computed tomography imaging and analysis in developmental biology and toxicology
Yousef Rezaeitabar,Ilkay Ulusoy
Birth Defects Research Part C: Embryo Today: Reviews.2011;99(2)1
[DOI]
272Micro-computed tomography imaging and analysis in developmental biology and toxicology
Chetna Kaushal,Md Khairul Islam,Anshu Singla,Md Al Amin
Birth Defects Research Part C: Embryo Today: Reviews.2022;99(2)177
[DOI]
273Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images
Ying-Hwey Nai,Bernice W. Teo,Nadya L. Tan,Koby Yi Wei Chua,Chun Kit Wong,Sophie O’Doherty,Mary C. Stephenson,Josh Schaefferkoetter,Yee Liang Thian,Edmund Chiong,Anthonin Reilhac,Nadia A. Chuzhanova
Computational and Mathematical Methods in Medicine.2020;2020(2)1
[DOI]
274Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images
Senthil Purushwalkam,Baihua Li,Qinggang Meng,Jamie McPhee
Computational and Mathematical Methods in Medicine.2013;7950(2)451
[DOI]
275Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based FuzzyC-Means Clustering
Ahmed Elazab,Changmiao Wang,Fucang Jia,Jianhuang Wu,Guanglin Li,Qingmao Hu
Computational and Mathematical Methods in Medicine.2015;2015(2)1
[DOI]
276Reverse engineering of human bones by using method of anatomical features
Vidosav Majstorovic,Miroslav Trajanovic,Nikola Vitkovic,Milos Stojkovic
CIRP Annals.2013;62(1)167
[DOI]
277Segmentation of MR Images of the Brain to Detect WM, GM, and CSF Tissues in the Presence of Noise and Intensity Inhomogeneity
Sandhya Gudise,Giri Babu Kande,Satya Savithri T.
IETE Journal of Research.2019;65(2)250
[DOI]
278COVID-opt-aiNet : A clinical decision support system for COVID -19 detection
Summrina Kanwal,Faiza Khan,Sultan Alamri,Kia Dashtipur,Mandar Gogate
International Journal of Imaging Systems and Technology.2022;65(2)250
[DOI]
279COVID-opt-aiNet : A clinical decision support system for COVID -19 detection
Ahmed Kharrat,Mohamed Ben Messaoud,Mohamed Abid,Karim Gasmi,Nacera Benamrane
International Journal of Imaging Systems and Technology.2010;65(2)369
[DOI]
280Directional weighted spatial fuzzy C-means for segmentation of brain MRI images
Sajid Ullah Khan,Imran Ullah,Imran Ahmed,Ali Imran,Najeeb Ullah
Journal of X-Ray Science and Technology.2020;27(6)1087
[DOI]
281Directional weighted spatial fuzzy C-means for segmentation of brain MRI images
Ahmed Kharrat,Karim Gasmi,Mohamed Ben Messaoud,Nacéra Benamrane,Mohamed Abid
Journal of X-Ray Science and Technology.2013;27(6)43
[DOI]
282Directional weighted spatial fuzzy C-means for segmentation of brain MRI images
Thuy Xuan Pham,Patrick Siarry,Hamouche Oulhadj
Journal of X-Ray Science and Technology.2018;221(6)359
[DOI]
283RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation
Rayyan Azam Khan,Yigang Luo,Fang-Xiang Wu
Artificial Intelligence in Medicine.2022;124(6)102231
[DOI]
284Methods on Skull Stripping of MRI Head Scan Images—a Review
P. Kalavathi,V. B. Surya Prasath
Journal of Digital Imaging.2016;29(3)365
[DOI]
285The Dimensions of the Orbital Cavity Based on High-Resolution Computed Tomography of Human Cadavers
Ulrik Ascanius Felding,Sune Land Bloch,Christian von Buchwald
Journal of Craniofacial Surgery.2016;27(4)1090
[DOI]
286A PAR-1–dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte
Richard M. Parton,Russell S. Hamilton,Graeme Ball,Lei Yang,C. Fiona Cullen,Weiping Lu,Hiroyuki Ohkura,Ilan Davis
Journal of Cell Biology.2011;194(1)121
[DOI]
287A PAR-1–dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte
Limali Sahoo,Lokanath Sarangi,Bidyut Ranjan Dash,Hemanta Kumar Palo
Journal of Cell Biology.2020;665(1)429
[DOI]
288A PAR-1–dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte
Yang Yu,Jiahao Wang,Ha Eun Chun,Yumeng Xu,Eliza Li Shan Fong,Aileen Wee,Hanry Yu
Journal of Cell Biology.2021;665(1)208
[DOI]
289A PAR-1–dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte
B. Perumal,J. Deny,Sindhiya Devi,V. Muneeswaran,Nagaraj. P
Journal of Cell Biology.2021;665(1)629
[DOI]
290An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification
D. Palani,K. Venkatalakshmi
Journal of Medical Systems.2019;43(2)629
[DOI]
291An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification
Yu Kong,Yueqin Dun,Jiandong Meng,Liang Wang,Wanqiang Zhang,Xinchun Li
Journal of Medical Systems.2020;633(2)107
[DOI]
292An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification
Tiago Esteves,Mariana Valente,Diana S. Nascimento,Perpétua Pinto-do-Ó,Pedro Quelhas
Journal of Medical Systems.2011;6669(2)151
[DOI]
293A new approach for fully automated segmentation of peripheral blood smears
Abdullah Elen,Muhammed Kamil Turan
International Journal of ADVANCED AND APPLIED SCIENCES.2018;5(1)81
[DOI]
294Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration
Fakhre Alam,Sami Ur Rahman
Journal of Medical Imaging and Radiation Sciences.2019;50(1)24
[DOI]
295Challenges and Solutions in Multimodal Medical Image Subregion Detection and Registration
Wenting Zhao,Lijin Wang,Yuxiao Shi,Xiaoming Xi,Yilong Yin,Yuchun Tang
Journal of Medical Imaging and Radiation Sciences.2016;50(1)20
[DOI]
296Computational fluid dynamic simulation of human carotid artery bifurcation based on anatomy and volumetric blood flow rate measured with magnetic resonance imaging
Hamidreza Gharahi,Byron A. Zambrano,David C. Zhu,J. Kevin DeMarco,Seungik Baek
International Journal of Advances in Engineering Sciences and Applied Mathematics.2016;8(1)46
[DOI]
297CT image segmentation methods for bone used in medical additive manufacturing
Maureen van Eijnatten,Roelof van Dijk,Johannes Dobbe,Geert Streekstra,Juha Koivisto,Jan Wolff
Medical Engineering & Physics.2018;51(1)6
[DOI]
298CT image segmentation methods for bone used in medical additive manufacturing
Pedro H. T. Gama,Hugo Oliveira,Jefersson A. dos Santos
Medical Engineering & Physics.2021;51(1)89
[DOI]
299A comparison of lateral ventricle volume estimation on magnetic resonance and cadaveric section images using the planimetry method
Orhan Bas,Selim Kayaci,Fatma Beyza Çeliker,Yilmaz Üçüncü,Mehmet Faik Özveren,Ali Yilmaz,Hilal Altas,Bunyamin Sahin
Journal of Clinical Neuroscience.2019;64(1)264
[DOI]
300A Combined Spatial Fuzzy C-Means and Level Set Approach for Endocardium Segmentation in MRI Image Series
Hossein Yousefi-Banaem,Saeed Kermani,Omid Srrafzadeh
Archives of Cardiovascular Imaging.2016;4(3)264
[DOI]
301Quantitative image analysis for evaluation of tumor response in clinical oncology
Wen-Li Cai,Guo-Bin Hong
Chronic Diseases and Translational Medicine.2018;4(1)18
[DOI]
302Outlier removal in biomaterial image segmentations using a non-stationary Bayesian learning
Wahyudin P. Syam,Panorios Benardos,Emily Britchford,Andrew Hopkinson,David T. Branson
Pattern Analysis and Applications.2021;24(4)1805
[DOI]
303Experimental assessment of phase aberration correction for breast MRgFUS therapy
Christopher R. Dillon,Alexis Farrer,Hailey McLean,Scott Almquist,Douglas Christensen,Allison Payne
International Journal of Hyperthermia.2018;34(6)731
[DOI]
304Experimental assessment of phase aberration correction for breast MRgFUS therapy
Christopher R. Dillon,Alexis Farrer,Hailey McLean,Scott Almquist,Douglas Christensen,Allison Payne
International Journal of Hyperthermia.2015;34(6)258
[DOI]
 
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