This paper is in the following e-collection/theme issue:

e-Learning and Medical Education (658) Epublishing and Open Access (80)

Published on in Vol 15, No 11 (2013): November

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Authors of this article:

Michael Kistler1 ;   Serena Bonaretti1, 2 ;   Marcel Pfahrer3 ;   Roman Niklaus3 ;   Philippe Büchler1
Article Authors Cited by (240) Tweetations (16) Metrics

Journals

  1. Ding Y, Li C, Yang Q, Qin Z, Qin Z. How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images. IEEE Access 2019;7:152821 View
  2. Khorram B, Yazdi M. A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation. Journal of Digital Imaging 2019;32(1):162 View
  3. Sriramakrishnan P, Kalaiselvi T, Rajeswaran R. Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering 2019;39(2):470 View
  4. Amin J, Sharif M, Raza M, Saba T, Anjum M. Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine 2019;177:69 View
  5. Essadike A, Ouabida E, Bouzid A. Optical scanning holography for tumor extraction from brain magnetic resonance images. Optics & Laser Technology 2020;127:106158 View
  6. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones T, Barrick T, Howe F, Ye X. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Computer Methods and Programs in Biomedicine 2018;157:69 View
  7. Shivhare S, Kumar N, Singh N. A hybrid of active contour model and convex hull for automated brain tumor segmentation in multimodal MRI. Multimedia Tools and Applications 2019;78(24):34207 View
  8. Agn M, Munck af Rosenschöld P, Puonti O, Lundemann M, Mancini L, Papadaki A, Thust S, Ashburner J, Law I, Van Leemput K. A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning. Medical Image Analysis 2019;54:220 View
  9. Eickel K, Porter D, Söhner A, Maaß M, Lüdemann L, Günther M, Degtyar V. Simultaneous multislice acquisition with multi-contrast segmented EPI for separation of signal contributions in dynamic contrast-enhanced imaging. PLOS ONE 2018;13(8):e0202673 View
  10. Zhou Z, He Z, Shi M, Du J, Chen D. 3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads. Computers in Biology and Medicine 2020;121:103766 View
  11. Chang Y, Foley P, Azimi V, Borkar R, Lefman J. Primer for Image Informatics in Personalized Medicine. Procedia Engineering 2016;159:58 View
  12. Egan P, Wang X, Greutert H, Shea K, Wuertz-Kozak K, Ferguson S. Mechanical and Biological Characterization of 3D Printed Lattices. 3D Printing and Additive Manufacturing 2019;6(2):73 View
  13. Sanghani P, Ti A, Kam King N, Ren H. Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients. Surgical Oncology 2019;29:178 View
  14. Montúfar J, Romero M, Scougall-Vilchis R. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American Journal of Orthodontics and Dentofacial Orthopedics 2018;153(3):449 View
  15. Winzeck S, Hakim A, McKinley R, Pinto J, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Paik M, Kwon Y, Lee H, Kim B, Won J, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly J, Lucas C, Heinrich M, Rivera L, Castillo L, Daza L, Beers A, Arbelaezs P, Maier O, Chang K, Brown J, Kalpathy-Cramer J, Zaharchuk G, Wiest R, Reyes M. ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI. Frontiers in Neurology 2018;9 View
  16. Alex V, Vaidhya K, Thirunavukkarasu S, Kesavadas C, Krishnamurthi G. Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation. Journal of Medical Imaging 2017;4(04):1 View
  17. Fischer M, Krooß F, Habor J, Radermacher K. A robust method for automatic identification of landmarks on surface models of the pelvis. Scientific Reports 2019;9(1) View
  18. Saba T, Sameh Mohamed A, El-Affendi M, Amin J, Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research 2020;59:221 View
  19. Sharif M, Amin J, Raza M, Anjum M, Afzal H, Shad S. Brain tumor detection based on extreme learning. Neural Computing and Applications 2020;32(20):15975 View
  20. Bougacha A, Boughariou J, Njeh I, Kammoun O, Mahfoudh K, Dammak M, Mhiri C, Hamida A. A RANK-TWO NMF CLUSTERING: APPLICATION TO GLIOBLASTOMAS CHARACTERIZATION AND COMPARATIVE STUDY. Biomedical Engineering: Applications, Basis and Communications 2019;31(03):1950019 View
  21. Maier O, Menze B, von der Gablentz J, Häni L, Heinrich M, Liebrand M, Winzeck S, Basit A, Bentley P, Chen L, Christiaens D, Dutil F, Egger K, Feng C, Glocker B, Götz M, Haeck T, Halme H, Havaei M, Iftekharuddin K, Jodoin P, Kamnitsas K, Kellner E, Korvenoja A, Larochelle H, Ledig C, Lee J, Maes F, Mahmood Q, Maier-Hein K, McKinley R, Muschelli J, Pal C, Pei L, Rangarajan J, Reza S, Robben D, Rueckert D, Salli E, Suetens P, Wang C, Wilms M, Kirschke J, Krämer U, Münte T, Schramm P, Wiest R, Handels H, Reyes M. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical Image Analysis 2017;35:250 View
  22. Yang T, Song J, Li L, Tang Q. Improving brain tumor segmentation on MRI based on the deep U-net and residual units. Journal of X-Ray Science and Technology 2020;28(1):95 View
  23. Banerjee S, Mitra S, Shankar B, Hayashi Y, Najbauer J. A Novel GBM Saliency Detection Model Using Multi-Channel MRI. PLOS ONE 2016;11(1):e0146388 View
  24. Mueller S, Kahrs L, Gaa J, Ortmaier T, Clausen J, Krettek C. Patient specific pointer tool for corrective osteotomy: Quality of symmetry based planning and case study of ulnar reconstruction surgery. Injury 2017;48(7):1325 View
  25. Lebre M, Vacavant A, Grand-Brochier M, Rositi H, Strand R, Rosier H, Abergel A, Chabrot P, Magnin B. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities. Computerized Medical Imaging and Graphics 2019;76:101635 View
  26. Stock M, Garvin H, Corron L, Hulse C, Cirillo L, Klales A, Colman K, Stull K. The importance of processing procedures and threshold values in CT scan segmentation of skeletal elements: An example using the immature os coxa. Forensic Science International 2020;309:110232 View
  27. Lebre M, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, Magnin B. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme. Computers in Biology and Medicine 2019;110:42 View
  28. Tong J, Zhao Y, Zhang P, Chen L, Jiang L. MRI brain tumor segmentation based on texture features and kernel sparse coding. Biomedical Signal Processing and Control 2019;47:387 View
  29. Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D. CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. Computer Methods and Programs in Biomedicine 2020;193:105524 View
  30. O’Connor J, Rutherford M, Hill J, Beverland D, Dunne N, Lennon A. Effect of combined flexion and external rotation on measurements of the proximal femur from anteroposterior pelvic radiographs. Orthopaedics & Traumatology: Surgery & Research 2018;104(4):449 View
  31. Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers 2019;11(9):1235 View
  32. Gupta S, Gupta R, Singla C. Analysis of image enhancement techniques for astrocytoma MRI images. International Journal of Information Technology 2017;9(3):311 View
  33. Gupta N, Bhatele P, Khanna P. Glioma detection on brain MRIs using texture and morphological features with ensemble learning. Biomedical Signal Processing and Control 2019;47:115 View
  34. Cattaneo C, Mazzarelli D, Cappella A, Castoldi E, Mattia M, Poppa P, De Angelis D, Vitello A, Biehler-Gomez L. A modern documented Italian identified skeletal collection of 2127 skeletons: the CAL Milano Cemetery Skeletal Collection. Forensic Science International 2018;287:219.e1 View
  35. Chaudhari A, Kulkarni J. Semi-automatic unsupervised MR brain tumour segmentation using a simple Bayesian Framework. The Imaging Science Journal 2019;67(8):434 View
  36. Kim K, Do W, Park S. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Medical Physics 2018;45(7):3120 View
  37. Tang Z, Xu Y, Jin L, Aibaidula A, Lu J, Jiao Z, Wu J, Zhang H, Shen D. Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients. IEEE Transactions on Medical Imaging 2020;39(6):2100 View
  38. Choi G, Shin J, Joohyun K, Kyung M, Lee Y. Data Augmentation Method for Deep Learning based Medical Image Segmentation Model. Journal of the Korea Computer Graphics Society 2019;25(3):123 View
  39. Pinto A, Pereira S, Rasteiro D, Silva C. Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognition 2018;82:105 View
  40. Bousselham A, Bouattane O, Youssfi M, Raihani A. Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area. International Journal of Biomedical Imaging 2019;2019:1 View
  41. Li C, Tan Y, Chen W, Luo X, He Y, Gao Y, Li F. ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation. Computers & Graphics 2020;90:11 View
  42. Kleesiek J, Petersen J, Döring M, Maier-Hein K, Köthe U, Wick W, Hamprecht F, Bendszus M, Biller A. Virtual Raters for Reproducible and Objective Assessments in Radiology. Scientific Reports 2016;6(1) View
  43. Krishan K, Kanchan T, Kharoshah M. “Advances in Forensic Anthropology” – Creation of skeletal databases for forensic anthropology research and casework. Egyptian Journal of Forensic Sciences 2016;6(2):29 View
  44. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones T, Barrick T, Howe F, Ye X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. International Journal of Computer Assisted Radiology and Surgery 2017;12(2):183 View
  45. Chaudhari A, Kulkarni J. Cerebral edema segmentation using textural feature. Biocybernetics and Biomedical Engineering 2019;39(3):599 View
  46. Klima O, Barina D, Kleparnik P, Zemcik P, Chromy A, Spanel M. Lossy Compression of 3D Statistical Shape and Intensity Models of Femoral Bones Using JPEG 2000. IFAC-PapersOnLine 2016;49(25):115 View
  47. Rajinikanth V, Dey N, Satapathy S, Ashour A. An approach to examine Magnetic Resonance Angiography based on Tsallis entropy and deformable snake model. Future Generation Computer Systems 2018;85:160 View
  48. Zhou Z, Siddiquee M, Tajbakhsh N, Liang J. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Transactions on Medical Imaging 2020;39(6):1856 View
  49. Amin J, Sharif M, Yasmin M, Saba T, Anjum M, Fernandes S. A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning. Journal of Medical Systems 2019;43(11) View
  50. Amin J, Sharif M, Anjum M, Raza M, Bukhari S. Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cognitive Systems Research 2020;59:304 View
  51. Mitra S, Banerjee S, Hayashi Y, Najbauer J. Volumetric brain tumour detection from MRI using visual saliency. PLOS ONE 2017;12(11):e0187209 View
  52. Molaie M, Zoroofi R. A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images. Journal of Digital Imaging 2020;33(5):1122 View
  53. Gerber N, Reyes M, Barazzetti L, Kjer H, Vera S, Stauber M, Mistrik P, Ceresa M, Mangado N, Wimmer W, Stark T, Paulsen R, Weber S, Caversaccio M, Ballester M. A multiscale imaging and modelling dataset of the human inner ear. Scientific Data 2017;4(1) View
  54. Girinon F, Gajny L, Ebrahimi S, Dagneaux L, Rouch P, Skalli W. Quasi-automated reconstruction of the femur from bi-planar X-rays. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020;8(5):529 View
  55. Wang F, Huang S, Shi L, Fan W. The application of series multi-pooling convolutional neural networks for medical image segmentation. International Journal of Distributed Sensor Networks 2017;13(12):155014771774889 View
  56. Bougacha A, Njeh I, Boughariou J, Kammoun O, Ben Mahfoudh K, Dammak M, Mhiri C, Ben Hamida A. Rank-Two NMF Clustering for Glioblastoma Characterization. Journal of Healthcare Engineering 2018;2018:1 View
  57. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 2017;4(1) View
  58. Ural B, Özışık P, Hardalaç F. An improved computer based diagnosis system for early detection of abnormal lesions in the brain tissues with using magnetic resonance and computerized tomography images. Multimedia Tools and Applications 2020;79(21-22):15613 View
  59. Bal A, Banerjee M, Chakrabarti A, Sharma P. MRI Brain Tumor Segmentation and Analysis using Rough-Fuzzy C-Means and Shape Based Properties. Journal of King Saud University - Computer and Information Sciences 2022;34(2):115 View
  60. Rehman Z, Naqvi S, Khan T, Khan M, Bashir T. Fully automated multi-parametric brain tumour segmentation using superpixel based classification. Expert Systems with Applications 2019;118:598 View
  61. Gupta N, Bhatele P, Khanna P. Identification of Gliomas from brain MRI through adaptive segmentation and run length of centralized patterns. Journal of Computational Science 2018;25:213 View
  62. Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q. Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognition 2018;83:134 View
  63. Hui H, Zhang X, Li F, Mei X, Guo Y. A Partitioning-Stacking Prediction Fusion Network Based on an Improved Attention U-Net for Stroke Lesion Segmentation. IEEE Access 2020;8:47419 View
  64. Ding Y, Gong L, Zhang M, Li C, Qin Z. A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing 2020;412:19 View
  65. Mzoughi H, Njeh I, Ben Slima M, Ben Hamida A, Mhiri C, Ben Mahfoudh K. Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors. Journal of Medical Imaging 2019;6(04):1 View
  66. Pereira S, Pinto A, Alves V, Silva C. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging 2016;35(5):1240 View
  67. Menze B, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M, Arbel T, Avants B, Ayache N, Buendia P, Collins D, Cordier N, Corso J, Criminisi A, Das T, Delingette H, Demiralp C, Durst C, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin K, Jena R, John N, Konukoglu E, Lashkari D, Mariz J, Meier R, Pereira S, Precup D, Price S, Raviv T, Reza S, Ryan M, Sarikaya D, Schwartz L, Shin H, Shotton J, Silva C, Sousa N, Subbanna N, Szekely G, Taylor T, Thomas O, Tustison N, Unal G, Vasseur F, Wintermark M, Ye D, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging 2015;34(10):1993 View
  68. Christodoulou N, Tousert N, Georgiadi E, Argyri K, Misichroni F, Stamatakos G. A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine. Cancer Informatics 2016;15:CIN.S40189 View
  69. Shehab L, Fahmy O, Gasser S, El-Mahallawy M. An efficient brain tumor image segmentation based on deep residual networks (ResNets). Journal of King Saud University - Engineering Sciences 2021;33(6):404 View
  70. Alom M, Yakopcic C, Hasan M, Taha T, Asari V. Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging 2019;6(01):1 View
  71. Zhou Z, He Z, Jia Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing 2020;402:235 View
  72. Wang Y, Li C, Zhu T, Zhang J. Multimodal brain tumor image segmentation using WRN-PPNet. Computerized Medical Imaging and Graphics 2019;75:56 View
  73. Li Y, Jia F, Qin J. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artificial Intelligence in Medicine 2016;73:1 View
  74. Clogenson M, Duff J, Luethi M, Levivier M, Meuli R, Baur C, Henein S. A statistical shape model of the human second cervical vertebra. International Journal of Computer Assisted Radiology and Surgery 2015;10(7):1097 View
  75. Inyang A, Roche S, Sivarasu S. An interpopulation comparison of 3-dimensional morphometric measurements of the proximal humerus. JSES International 2020;4(3):453 View
  76. Klima O, Chromy A, Zemcik P, Spanel M, Kleparnik P. A Study on Performace of Levenberg-Marquardt and CMA-ES Optimization Methods for Atlas-based 2D/3D Reconstruction. IFAC-PapersOnLine 2016;49(25):121 View
  77. Banerjee S, Mitra S, Uma Shankar B. Automated 3D segmentation of brain tumor using visual saliency. Information Sciences 2018;424:337 View
  78. Hu K, Gan Q, Zhang Y, Deng S, Xiao F, Huang W, Cao C, Gao X. Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field. IEEE Access 2019;7:92615 View
  79. Mamabolo B, Alblas A, Brits D. Modern imaging modalities in forensic anthropology and the potential of low-dose X-rays. Forensic Imaging 2020;23:200406 View
  80. Pinto A, Pereira S, Meier R, Wiest R, Alves V, Reyes M, Silva C. Combining unsupervised and supervised learning for predicting the final stroke lesion. Medical Image Analysis 2021;69:101888 View
  81. O'Connor J, Hill J, Beverland D, Dunne N, Lennon A. Influence of preoperative femoral orientation on radiographic measures of femoral head height in total hip replacement. Clinical Biomechanics 2021;81:105247 View
  82. Alipour N, Hasanzadeh R. Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model. Multimedia Tools and Applications 2021;80(6):8835 View
  83. Kavur A, Gezer N, Barış M, Aslan S, Conze P, Groza V, Pham D, Chatterjee S, Ernst P, Özkan S, Baydar B, Lachinov D, Han S, Pauli J, Isensee F, Perkonigg M, Sathish R, Rajan R, Sheet D, Dovletov G, Speck O, Nürnberger A, Maier-Hein K, Bozdağı Akar G, Ünal G, Dicle O, Selver M. CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis 2021;69:101950 View
  84. Goin B, Renault J, Thibes L, Chabrand P. Influence of material properties and boundary conditions on patient-specific models. Computer Methods in Biomechanics and Biomedical Engineering 2021;24(4):429 View
  85. Fischer M, Grothues S, Habor J, de la Fuente M, Radermacher K. A robust method for automatic identification of femoral landmarks, axes, planes and bone coordinate systems using surface models. Scientific Reports 2020;10(1) View
  86. Ahmadi M, Sharifi A, Hassantabar S, Enayati S, Zhou H. QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network. BioMed Research International 2021;2021:1 View
  87. Ding Y, Wu G, Chen D, Zhang N, Gong L, Cao M, Qin Z. DeepEDN: A Deep-Learning-Based Image Encryption and Decryption Network for Internet of Medical Things. IEEE Internet of Things Journal 2021;8(3):1504 View
  88. Sandhya G, Srinag A, Pantangi G, Kanaparthi J. Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features. Journal of Biomimetics, Biomaterials and Biomedical Engineering 2021;49:63 View
  89. Biratu E, Schwenker F, Debelee T, Kebede S, Negera W, Molla H. Enhanced Region Growing for Brain Tumor MR Image Segmentation. Journal of Imaging 2021;7(2):22 View
  90. Sharif M, Li J, Amin J, Sharif A. An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network. Complex & Intelligent Systems 2021;7(4):2023 View
  91. Kaushal B, Patil M, Birajdar G. Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging. International Journal of Imaging Systems and Technology 2021;31(2):575 View
  92. Savoldi F, Montalvao C, Hui L, Leung C, Jablonski N, Tsoi J, Bornstein M. The Human Bone Collection of the Faculty of Dentistry at the University of Hong Kong: History and description of cranial and postcranial skeletal remains. American Journal of Physical Anthropology 2021;175(3):718 View
  93. Chikhalikar A, Dharwadkar N. Model for Enhancement and Segmentation of Magnetic Resonance Images for Brain Tumor Classification. Pattern Recognition and Image Analysis 2021;31(1):49 View
  94. Bhatele K, Bhadauria S. Machine learning application in Glioma classification: review and comparison analysis. Archives of Computational Methods in Engineering 2022;29(1):247 View
  95. Min S, Chen X, Xie H, Zha Z, Zhang Y. A Mutually Attentive Co-Training Framework for Semi-Supervised Recognition. IEEE Transactions on Multimedia 2021;23:899 View
  96. Wu D, Ren H, Li Q. Self-Supervised Dynamic CT Perfusion Image Denoising With Deep Neural Networks. IEEE Transactions on Radiation and Plasma Medical Sciences 2021;5(3):350 View
  97. Molaie M, Aghaeizadeh Zoroofi R. Thigh muscle segmentation using a hybrid FRFCM‐based multi‐atlas method and morphology‐based interpolation algorithm. IET Image Processing 2021;15(11):2572 View
  98. Shivhare S, Kumar N. Tumor bagging: a novel framework for brain tumor segmentation using metaheuristic optimization algorithms. Multimedia Tools and Applications 2021;80(17):26969 View
  99. Wu X, Bi L, Fulham M, Feng D, Zhou L, Kim J. Unsupervised brain tumor segmentation using a symmetric-driven adversarial network. Neurocomputing 2021;455:242 View
  100. Singh R, Goel A, Raghuvanshi D. Binary glioma grading framework employing locality preserving projections and Gaussian radial basis function support vector machine. International Journal of Imaging Systems and Technology 2021;31(4):2047 View
  101. Zhang X, Hu Y, Chen W, Huang G, Nie S. 3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks. Journal of Zhejiang University-SCIENCE B 2021;22(6):462 View
  102. Asvadi A, Dardenne G, Troccaz J, Burdin V. Bone surface reconstruction and clinical features estimation from sparse landmarks and Statistical Shape Models: a feasibility study on the femur. Medical Engineering & Physics 2021;95:30 View
  103. de la Rosa E, Sima D, Menze B, Kirschke J, Robben D. AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning. Medical Image Analysis 2021;74:102211 View
  104. Berger L, Pahr D, Synek A. Articular contact vs. embedding: Effect of simplified boundary conditions on the stress distribution in the distal radius and volar plate implant loading. Journal of Biomechanics 2022;143:111279 View
  105. Valizadeh A, Shariatee M, Manic S. The Progress of Medical Image Semantic Segmentation Methods for Application in COVID-19 Detection. Computational Intelligence and Neuroscience 2021;2021:1 View
  106. Cheng J, Kuang H, Zhao Q, Wang Y, Xu L, Liu J, Wang J. DWT-CV: Dense weight transfer-based cross validation strategy for model selection in biomedical data analysis. Future Generation Computer Systems 2022;135:20 View
  107. Kriechling P, Leoty L, Fürnstahl P, Rahbani D, Zingg P, Vlachopoulos L. A Statistical Shape Model-Based Analysis of Periacetabular Osteotomies. Journal of Bone and Joint Surgery 2022;104(12):1107 View
  108. Bao X, Zhao C, Bao S, Rao J, Yang Z, Li X. Recognition of necrotic regions in MRI images of chronic spinal cord injury based on superpixel. Computer Methods and Programs in Biomedicine 2023;228:107252 View
  109. Preethi Saroj S, Gurunathan P. Cascaded layer-coalescing convolution network for brain tumor segmentation. Journal of Intelligent & Fuzzy Systems 2022;43(4):5293 View
  110. Glenday J, Sivarasu S, Roche S, Kontaxis A. Development of a framework to assess the biomechanical impact of reverse shoulder arthroplasty placement modifications. Journal of Orthopaedic Research 2022;40(9):2156 View
  111. Sambath Kumar K, Rajendran A. An automatic brain tumor segmentation using modified inception module based U-Net model. Journal of Intelligent & Fuzzy Systems 2022;42(3):2743 View
  112. Mittermeier A, Reidler P, Fabritius M, Schachtner B, Wesp P, Ertl-Wagner B, Dietrich O, Ricke J, Kellert L, Tiedt S, Kunz W, Ingrisch M. End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics 2022;12(5):1142 View
  113. Liu P, Lee Y, Aylward S, Niethammer M. Perfusion Imaging: An Advection Diffusion Approach. IEEE Transactions on Medical Imaging 2021;40(12):3424 View
  114. Maurya S, Tiwari S, Mothukuri M, Tangeda C, Nandigam R, Addagiri D. A review on recent developments in cancer detection using Machine Learning and Deep Learning models. Biomedical Signal Processing and Control 2023;80:104398 View
  115. Amin J, Anjum M, Sharif M, Jabeen S, Kadry S, Moreno Ger P, Loddo A. A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier. Computational Intelligence and Neuroscience 2022;2022:1 View
  116. Nauyan Rashid S, Hanif M, Habib U, Khalil A, Inam O, Ur Rehman H. Early-Stage Segmentation and Characterization of Brain Tumor. Computers, Materials & Continua 2022;73(1):1001 View
  117. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak R. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems 2022;8(4):3161 View
  118. Niyas S, Pawan S, Anand Kumar M, Rajan J. Medical image segmentation with 3D convolutional neural networks: A survey. Neurocomputing 2022;493:397 View
  119. Gryska E, Björkman-Burtscher I, Jakola A, Dunås T, Schneiderman J, Heckemann R. Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study. BMJ Open 2022;12(7):e059000 View
  120. Zanier O, Da Mutten R, Vieli M, Regli L, Serra C, Staartjes V. DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning. Acta Neurochirurgica 2022;165(2):555 View
  121. Messaoudi R, Jaziri F, Mtibaa A, Gargouri F, Vacavant A. Ontology-Driven Approach for Liver MRI Classification and HCC Detection. International Journal of Pattern Recognition and Artificial Intelligence 2021;35(12) View
  122. Hou Z, Zhou X. Joint Adaptation of ICP Proposal and Target Distribution for Probabilistic Surface Registration. IEEE Signal Processing Letters 2022;29:259 View
  123. Li X, Jiang Y, Li M, Zhang J, Yin S, Luo H. MSFR‐Net: Multi‐modality and single‐modality feature recalibration network for brain tumor segmentation. Medical Physics 2023;50(4):2249 View
  124. Wolf D, Regnery S, Tarnawski R, Bobek-Billewicz B, Polańska J, Götz M. Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation. Applied Sciences 2022;12(21):10763 View
  125. Alpar O. A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging. Expert Systems with Applications 2023;216:119462 View
  126. Gajny L, Girinon F, Bayoud W, Lahkar B, Bonnet-Lebrun A, Rouch P, Lazennec J, Skalli W. Fast quasi-automated 3D reconstruction of lower limbs from low dose biplanar radiographs using statistical shape models and contour matching. Medical Engineering & Physics 2022;101:103769 View
  127. Zhou C, Ding C, Wang X, Lu Z, Tao D. One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation. IEEE Transactions on Image Processing 2020;29:4516 View
  128. Ge T, Zhan T, Li Q, Mu S, Conforto S. Optimal Superpixel Kernel-Based Kernel Low-Rank and Sparsity Representation for Brain Tumour Segmentation. Computational Intelligence and Neuroscience 2022;2022:1 View
  129. Satrya G, Ramatryana I, Shin S. Compressive Sensing of Medical Images Based on HSV Color Space. Sensors 2023;23(5):2616 View
  130. Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi A. Medical image segmentation using deep learning: A survey. IET Image Processing 2022;16(5):1243 View
  131. ATASEVER S, AZGINOGLU N, TERZI D, TERZI R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clinical Imaging 2023;94:18 View
  132. Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing 2022;506:311 View
  133. Huang L, Zhu E, Chen L, Wang Z, Chai S, Zhang B. A transformer-based generative adversarial network for brain tumor segmentation. Frontiers in Neuroscience 2022;16 View
  134. Loução R, Oros-Peusquens A, Langen K, Ferreira H, Shah N. A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours. Frontiers in Oncology 2021;11 View
  135. Amin J, Anjum M, Gul N, Sharif M. A secure two-qubit quantum model for segmentation and classification of brain tumor using MRI images based on blockchain. Neural Computing and Applications 2022;34(20):17315 View
  136. Lather M, Singh P. Tumor segmentation from brain MR images using STSA based modified K-means clustering approach. Journal of Intelligent & Fuzzy Systems 2022;43(3):2579 View
  137. Guezou-Philippe A, Dardenne G, Letissier H, Yvinou A, Burdin V, Stindel E, Lefèvre C. Anterior pelvic plane estimation for total hip arthroplasty using a joint ultrasound and statistical shape model based approach. Medical & Biological Engineering & Computing 2023;61(1):195 View
  138. Litavec H, Basom R. Incorporating a structural vulnerability framework into the forensic anthropology curriculum. Forensic Science International: Synergy 2023;6:100320 View
  139. Yilmaz V, Akdag M, Dalveren Y, Doruk R, Kara A, Soylu A. Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images. Diagnostics 2023;13(4):651 View
  140. Ahmad P, Jin H, Alroobaea R, Qamar S, Zheng R, Alnajjar F, Aboudi F. MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation. IEEE Access 2021;9:148384 View
  141. Alpar O, Dolezal R, Ryska P, Krejcar O. Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means. Applied Intelligence 2022;52(13):15237 View
  142. Boutillon A, Salhi A, Burdin V, Borotikar B. Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning. IEEE Transactions on Biomedical Engineering 2022;69(9):2733 View
  143. Swaraja K, Meenakshi K, Valiveti H, Karuna G. Segmentation and detection of brain tumor through optimal selection of integrated features using transfer learning. Multimedia Tools and Applications 2022;81(19):27363 View
  144. Wen B, Hu P, Ebrahimi M, Chen H. Key Factors Affecting User Adoption of Open-Access Data Repositories in Intelligence and Security Informatics: An Affordance Perspective. ACM Transactions on Management Information Systems 2022;13(1):1 View
  145. Bhatele K, Bhadauria S. Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning. Multimedia Tools and Applications 2023;82(3):3831 View
  146. Valenzuela W, Balsiger F, Wiest R, Scheidegger O. Medical-Blocks―A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research: System Development and Integration Results. JMIR Formative Research 2022;6(4):e32287 View
  147. Deepika M, Raajan N, Srinivasan A. Three dimensional reconstruction of brain tumor along with space occupying in lesions. Multimedia Tools and Applications 2022;81(9):12701 View
  148. Wang H, Dong L, Song W, Zhao X, Xia J, Liu T. Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image. Intelligent Automation & Soft Computing 2022;32(3):1573 View
  149. Alpar O, Dolezal R, Ryska P, Krejcar O. Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI. Pattern Recognition 2022;128:108675 View
  150. Kumar A, Ghosal P, Kundu S, Mukherjee A, Nandi D. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer Methods and Programs in Biomedicine 2022;226:107157 View
  151. Natarajan S, Govindaraj V, Zhang Y, Murugan P, Balasubramanian K, Kandasamy K, Ejaz K. Minimally parametrized segmentation framework with dual metaheuristic optimisation algorithms and FCM for detection of anomalies in MR brain images. Biomedical Signal Processing and Control 2022;78:103866 View
  152. Niu J, Tan Q, Zou X, Jin S. Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method. Mathematical Biosciences and Engineering 2022;20(2):2890 View
  153. Fischer M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 2023;10(1) View
  154. Xu Z, Ding C. Combining convolutional attention mechanism and residual deformable Transformer for infarct segmentation from CT scans of acute ischemic stroke patients. Frontiers in Neurology 2023;14 View
  155. Iporre-Rivas A, Saur D, Rohr K, Scheuermann G, Gillmann C. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. Journal of Medical Imaging 2023;10(04) View
  156. Wani J, Sofi T, Sofi I, Ganaie S. The status of open access repositories in the field of technology: insights from OpenDOAR. Information Discovery and Delivery 2024;52(2):164 View
  157. Preetha R, Priyadarsini M, Nisha J. Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images. IEEE Access 2023;11:138549 View
  158. Fan F, Ritschl L, Beister M, Biniazan R, Wagner F, Kreher B, Gottschalk T, Kappler S, Maier A. Simulation‐driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans. Medical Physics 2024;51(5):3360 View
  159. Lee J, Nishikawa R. Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover. Breast Cancer Research 2024;26(1) View
  160. Alpar O. Content-aware Nakagami morphing for incremental brain MRI. Knowledge-Based Systems 2024;291:111575 View
  161. Zhang K, Zhu Y, Li H, Zeng Z, Liu Y, Zhang Y. MDANet: Multimodal difference aware network for brain stroke segmentation. Biomedical Signal Processing and Control 2024;95:106383 View
  162. Verma A, Shivhare S, Singh S, Kumar N, Nayyar A. Comprehensive Review on MRI-Based Brain Tumor Segmentation: A Comparative Study from 2017 Onwards. Archives of Computational Methods in Engineering 2024 View

Books/Policy Documents

  1. Bousselham A, Bouattane O, Youssfi M, Raihani A. Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). View
  2. Agn M, Puonti O, Rosenschöld P, Law I, Van Leemput K. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  3. Bakas S, Zeng K, Sotiras A, Rathore S, Akbari H, Gaonkar B, Rozycki M, Pati S, Davatzikos C. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  4. Bal A, Banerjee M, Sharma P, Chaki R. Advanced Computing and Systems for Security. View
  5. Chen X, Liew J, Xiong W, Chui C, Ong S. Computer Vision – ECCV 2018. View
  6. Rezaei M, Yang H, Meinel C. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  7. Pritanjli , Doegar A. Recent Trends in Communication and Intelligent Systems. View
  8. Bernardino G, Butakoff C, Nuñez-Garcia M, Sarvari S, Rodriguez-Lopez M, Crispi F, González Ballester M, De Craene M, Bijnens B. Functional Imaging and Modelling of the Heart. View
  9. Lebre M, Arrouk K, Võ Văn A, Leborgne A, Grand-Brochier M, Beaurepaire P, Vacavant A, Magnin B, Abergel A, Chabrot P. Simulation and Synthesis in Medical Imaging. View
  10. Kalra M, Osadebey M, Bouguila N, Pedersen M, Fan W. Mixture Models and Applications. View
  11. Kao P, Chen J, Manjunath B. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  12. Klíma O, Madeja R, Španel M, Čuta M, Zemčík P, Stoklásek P, Mizera A. Shape in Medical Imaging. View
  13. Jimenez D, García H, Álvarez A, Orozco Á. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. View
  14. Kleesiek J, Biller A, Bartsch A, Ueltzhöffer K. Biomedical Engineering Systems and Technologies. View
  15. O’Connor J, Rutherford M, Hill J, Beverland D, Dunne N, Lennon A. Computer Methods in Biomechanics and Biomedical Engineering. View
  16. Tureckova A, Rodríguez-Sánchez A. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  17. Mohan G, Subashini M. Deep Learning and Parallel Computing Environment for Bioengineering Systems. View
  18. Saha R, Phophalia A, Mitra S. Computer Vision, Graphics, and Image Processing. View
  19. Kong X, Sun G, Wu Q, Liu J, Lin F. Intelligent Information Processing IX. View
  20. Abler D, Rockne R, Büchler P. New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering. View
  21. Zeng K, Bakas S, Sotiras A, Akbari H, Rozycki M, Rathore S, Pati S, Davatzikos C. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  22. Jimenez D, García H, Álvarez A, Orozco Á, Holguín G. Image Analysis and Recognition. View
  23. Pereira S, Pinto A, Alves V, Silva C. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  24. Shivhare S, Kumar N. Proceedings of ICETIT 2019. View
  25. Pinheiro G, Voltoline R, Bento M, Rittner L. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  26. Fabijańska A, Vacavant A, Lebre M, Pavan A, de Pina D, Abergel A, Chabrot P, Magnin B. Computer Vision and Graphics. View
  27. Bousselham A, Bouattane O, Youssfi M, Raihani A. Embedded Systems and Artificial Intelligence. View
  28. Liu P, Lee Y, Aylward S, Niethammer M. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. View
  29. de la Rosa E, Robben D, Sima D, Kirschke J, Menze B. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. View
  30. Madsen D, Aellen J, Morel-Forster A, Vetter T, Lüthi M. Shape in Medical Imaging. View
  31. Messaoudi R, Jaziri F, Vacavant A, Mtibaa A, Gargouri F. Pattern Recognition and Artificial Intelligence. View
  32. Madsen D, Morel-Forster A, Kahr P, Rahbani D, Vetter T, Lüthi M. Computer Vision – ECCV 2020. View
  33. Klug J, Leclerc G, Dirren E, Preti M, Van De Ville D, Carrera E. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. View
  34. Shivhare S, Kumar N. Progress in Advanced Computing and Intelligent Engineering. View
  35. Kaushal B, Patil M, Birajdar G. Handbook of Computational Intelligence in Biomedical Engineering and Healthcare. View
  36. Verma A. Advanced Healthcare Systems. View
  37. Kalaichelvi N, Kalaiselvi T, Somasundaram K. Applied Smart Health Care Informatics. View
  38. Sharma P, Goyal D, Tiwari N. Congress on Intelligent Systems. View
  39. Verma A. Deep Learning Technologies for the Sustainable Development Goals. View
  40. Shivhare S, Kumar N. Proceedings of Academia-Industry Consortium for Data Science. View
  41. Alpar O, Krejcar O. Bioinformatics and Biomedical Engineering. View
  42. Alpar O, Krejcar O. Bioinformatics and Biomedical Engineering. View
  43. Halder R, Sharmin N. Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. View