Explore our sister journals here.

Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 12.11.13 in Vol 15, No 11 (2013): November

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

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

Michael Kistler, Serena Bonaretti, Marcel Pfahrer, Roman Niklaus, Philippe Büchler

J Med Internet Res 2013 (Nov 12); 15(11):e245 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.2930):

(note that this is only a small subset of citations)

  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  4. Amin J, Sharif M, Raza M, Saba T, Anjum MA. Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine 2019;177:69
    CrossRef
  5. Essadike A, Ouabida E, Bouzid A. Optical scanning holography for tumor extraction from brain magnetic resonance images. Optics & Laser Technology 2020;127:106158
    CrossRef
  6. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Computer Methods and Programs in Biomedicine 2018;157:69
    CrossRef
  7. Shivhare SN, 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
    CrossRef
  8. Agn M, Munck af Rosenschöld P, Puonti O, Lundemann MJ, 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
    CrossRef
  9. Eickel K, Porter DA, Söhner A, Maaß M, Lüdemann L, Günther M, Degtyar VE. Simultaneous multislice acquisition with multi-contrast segmented EPI for separation of signal contributions in dynamic contrast-enhanced imaging. PLOS ONE 2018;13(8):e0202673
    CrossRef
  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
    CrossRef
  11. Chang YH, Foley P, Azimi V, Borkar R, Lefman J. Primer for Image Informatics in Personalized Medicine. Procedia Engineering 2016;159:58
    CrossRef
  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
    CrossRef
  13. Sanghani P, Ti AB, Kam King NK, Ren H. Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients. Surgical Oncology 2019;29:178
    CrossRef
  14. Montúfar J, Romero M, Scougall-Vilchis RJ. Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. American Journal of Orthodontics and Dentofacial Orthopedics 2018;153(3):449
    CrossRef
  15. Winzeck S, Hakim A, McKinley R, Pinto JAADSR, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Paik MC, Kwon Y, Lee H, Kim BJ, Won J, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly JM, Lucas C, Heinrich MP, Rivera LC, Castillo LS, Daza LA, Beers AL, Arbelaezs P, Maier O, Chang K, Brown JM, 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
    CrossRef
  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
    CrossRef
  17. Fischer MCM, 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)
    CrossRef
  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
    CrossRef
  19. Sharif M, Amin J, Raza M, Anjum MA, Afzal H, Shad SA. Brain tumor detection based on extreme learning. Neural Computing and Applications 2020;32(20):15975
    CrossRef
  20. Bougacha A, Boughariou J, Njeh I, Kammoun O, Mahfoudh KB, Dammak M, Mhiri C, Hamida AB. A RANK-TWO NMF CLUSTERING: APPLICATION TO GLIOBLASTOMAS CHARACTERIZATION AND COMPARATIVE STUDY. Biomedical Engineering: Applications, Basis and Communications 2019;31(03):1950019
    CrossRef
  21. Maier O, Menze BH, von der Gablentz J, Häni L, Heinrich MP, 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 KM, Jodoin P, Kamnitsas K, Kellner E, Korvenoja A, Larochelle H, Ledig C, Lee J, Maes F, Mahmood Q, Maier-Hein KH, McKinley R, Muschelli J, Pal C, Pei L, Rangarajan JR, Reza SM, Robben D, Rueckert D, Salli E, Suetens P, Wang C, Wilms M, Kirschke JS, Krämer UM, Münte TF, 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
    CrossRef
  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
    CrossRef
  23. Banerjee S, Mitra S, Shankar BU, Hayashi Y, Najbauer J. A Novel GBM Saliency Detection Model Using Multi-Channel MRI. PLOS ONE 2016;11(1):e0146388
    CrossRef
  24. Mueller S, Kahrs LA, 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
    CrossRef
  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
    CrossRef
  26. Stock MK, Garvin HM, Corron LK, Hulse CN, Cirillo LE, Klales AR, Colman KL, Stull KE. 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  31. Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers 2019;11(9):1235
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  36. Kim KH, Do W, Park S. Improving resolution of MR images with an adversarial network incorporating images with different contrast. Medical Physics 2018;45(7):3120
    CrossRef
  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
    CrossRef
  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
    CrossRef
  39. Pinto A, Pereira S, Rasteiro D, Silva CA. Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognition 2018;82:105
    CrossRef
  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
    CrossRef
  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
    CrossRef
  42. Kleesiek J, Petersen J, Döring M, Maier-Hein K, Köthe U, Wick W, Hamprecht FA, Bendszus M, Biller A. Virtual Raters for Reproducible and Objective Assessments in Radiology. Scientific Reports 2016;6(1)
    CrossRef
  43. Krishan K, Kanchan T, Kharoshah MA. “Advances in Forensic Anthropology” – Creation of skeletal databases for forensic anthropology research and casework. Egyptian Journal of Forensic Sciences 2016;6(2):29
    CrossRef
  44. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, 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
    CrossRef
  45. Chaudhari A, Kulkarni J. Cerebral edema segmentation using textural feature. Biocybernetics and Biomedical Engineering 2019;39(3):599
    CrossRef
  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
    CrossRef
  47. Rajinikanth V, Dey N, Satapathy SC, Ashour AS. An approach to examine Magnetic Resonance Angiography based on Tsallis entropy and deformable snake model. Future Generation Computer Systems 2018;85:160
    CrossRef
  48. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation. IEEE Transactions on Medical Imaging 2020;39(6):1856
    CrossRef
  49. Amin J, Sharif M, Yasmin M, Saba T, Anjum MA, Fernandes SL. A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning. Journal of Medical Systems 2019;43(11)
    CrossRef
  50. Amin J, Sharif M, Anjum MA, Raza M, Bukhari SAC. Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cognitive Systems Research 2020;59:304
    CrossRef
  51. Mitra S, Banerjee S, Hayashi Y, Najbauer J. Volumetric brain tumour detection from MRI using visual saliency. PLOS ONE 2017;12(11):e0187209
    CrossRef
  52. Molaie M, Zoroofi RA. 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
    CrossRef
  53. Gerber N, Reyes M, Barazzetti L, Kjer HM, Vera S, Stauber M, Mistrik P, Ceresa M, Mangado N, Wimmer W, Stark T, Paulsen RR, Weber S, Caversaccio M, Ballester MAG. A multiscale imaging and modelling dataset of the human inner ear. Scientific Data 2017;4(1)
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  57. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 2017;4(1)
    CrossRef
  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
    CrossRef
  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
    CrossRef
  60. Rehman ZU, Naqvi SS, Khan TM, Khan MA, Bashir T. Fully automated multi-parametric brain tumour segmentation using superpixel based classification. Expert Systems with Applications 2019;118:598
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  66. Pereira S, Pinto A, Alves V, Silva CA. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging 2016;35(5):1240
    CrossRef
  67. Menze BH, 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 BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, 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
    CrossRef
  68. Christodoulou NA, Tousert NE, Georgiadi EC, Argyri KD, Misichroni FD, Stamatakos GS. 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
    CrossRef
  69. Shehab LH, Fahmy OM, Gasser SM, El-Mahallawy MS. An efficient brain tumor image segmentation based on deep residual networks (ResNets). Journal of King Saud University - Engineering Sciences 2021;33(6):404
    CrossRef
  70. Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK. Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging 2019;6(01):1
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  74. Clogenson M, Duff JM, 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
    CrossRef
  75. Inyang AO, Roche S, Sivarasu S. An interpopulation comparison of 3-dimensional morphometric measurements of the proximal humerus. JSES International 2020;4(3):453
    CrossRef
  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
    CrossRef
  77. Banerjee S, Mitra S, Uma Shankar B. Automated 3D segmentation of brain tumor using visual saliency. Information Sciences 2018;424:337
    CrossRef
  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
    CrossRef
  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
    CrossRef
  80. Pinto A, Pereira S, Meier R, Wiest R, Alves V, Reyes M, Silva CA. Combining unsupervised and supervised learning for predicting the final stroke lesion. Medical Image Analysis 2021;69:101888
    CrossRef
  81. O'Connor JD, Hill JC, Beverland DE, Dunne NJ, Lennon AB. Influence of preoperative femoral orientation on radiographic measures of femoral head height in total hip replacement. Clinical Biomechanics 2021;81:105247
    CrossRef
  82. Alipour N, Hasanzadeh RPR. Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model. Multimedia Tools and Applications 2021;80(6):8835
    CrossRef
  83. Kavur AE, Gezer NS, Barış M, Aslan S, Conze P, Groza V, Pham DD, 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 KH, Bozdağı Akar G, Ünal G, Dicle O, Selver MA. CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis 2021;69:101950
    CrossRef
  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
    CrossRef
  85. Fischer MCM, Grothues SAGA, 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)
    CrossRef
  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
    CrossRef
  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
    CrossRef
  88. Sandhya G, Srinag A, Pantangi GB, Kanaparthi JA. Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features. Journal of Biomimetics, Biomaterials and Biomedical Engineering 2021;49:63
    CrossRef
  89. Biratu ES, Schwenker F, Debelee TG, Kebede SR, Negera WG, Molla HT. Enhanced Region Growing for Brain Tumor MR Image Segmentation. Journal of Imaging 2021;7(2):22
    CrossRef
  90. Sharif MI, Li JP, 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
    CrossRef
  91. Kaushal B, Patil MD, Birajdar GK. Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging. International Journal of Imaging Systems and Technology 2021;31(2):575
    CrossRef
  92. Savoldi F, Montalvao C, Hui L, Leung CK, Jablonski NG, Tsoi JK, Bornstein MM. 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
    CrossRef
  93. Chikhalikar AM, Dharwadkar NV. Model for Enhancement and Segmentation of Magnetic Resonance Images for Brain Tumor Classification. Pattern Recognition and Image Analysis 2021;31(1):49
    CrossRef
  94. Bhatele KR, Bhadauria SS. Machine learning application in Glioma classification: review and comparison analysis. Archives of Computational Methods in Engineering 2022;29(1):247
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  98. Shivhare SN, Kumar N. Tumor bagging: a novel framework for brain tumor segmentation using metaheuristic optimization algorithms. Multimedia Tools and Applications 2021;80(17):26969
    CrossRef
  99. Wu X, Bi L, Fulham M, Feng DD, Zhou L, Kim J. Unsupervised brain tumor segmentation using a symmetric-driven adversarial network. Neurocomputing 2021;455:242
    CrossRef
  100. Singh R, Goel A, Raghuvanshi DK. 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  103. de la Rosa E, Sima DM, Menze B, Kirschke JS, Robben D. AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning. Medical Image Analysis 2021;74:102211
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  107. Kriechling P, Leoty L, Fürnstahl P, Rahbani D, Zingg PO, Vlachopoulos L. A Statistical Shape Model-Based Analysis of Periacetabular Osteotomies. Journal of Bone and Joint Surgery 2022;104(12):1107
    CrossRef
  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
    CrossRef
  109. Preethi Saroj S, Gurunathan P. Cascaded layer-coalescing convolution network for brain tumor segmentation. Journal of Intelligent & Fuzzy Systems 2022;43(4):5293
    CrossRef
  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
    CrossRef
  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
    CrossRef
  112. Mittermeier A, Reidler P, Fabritius MP, Schachtner B, Wesp P, Ertl-Wagner B, Dietrich O, Ricke J, Kellert L, Tiedt S, Kunz WG, 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
    CrossRef
  113. Liu P, Lee YZ, Aylward SR, Niethammer M. Perfusion Imaging: An Advection Diffusion Approach. IEEE Transactions on Medical Imaging 2021;40(12):3424
    CrossRef
  114. Maurya S, Tiwari S, Mothukuri MC, Tangeda CM, Nandigam RNS, Addagiri DC. A review on recent developments in cancer detection using Machine Learning and Deep Learning models. Biomedical Signal Processing and Control 2023;80:104398
    CrossRef
  115. Amin J, Anjum MA, 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
    CrossRef
  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
    CrossRef
  117. Amin J, Sharif M, Haldorai A, Yasmin M, Nayak RS. Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & Intelligent Systems 2022;8(4):3161
    CrossRef
  118. Niyas S, Pawan S, Anand Kumar M, Rajan J. Medical image segmentation with 3D convolutional neural networks: A survey. Neurocomputing 2022;493:397
    CrossRef
  119. Gryska E, Björkman-Burtscher I, Jakola AS, Dunås T, Schneiderman J, Heckemann RA. 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
    CrossRef
  120. Zanier O, Da Mutten R, Vieli M, Regli L, Serra C, Staartjes VE. DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning. Acta Neurochirurgica 2022;165(2):555
    CrossRef
  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)
    CrossRef
  122. Hou Z, Zhou X. Joint Adaptation of ICP Proposal and Target Distribution for Probabilistic Surface Registration. IEEE Signal Processing Letters 2022;29:259
    CrossRef
  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
    CrossRef
  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
    CrossRef
  125. . A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging. Expert Systems with Applications 2023;216:119462
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  129. Satrya GB, Ramatryana INA, Shin SY. Compressive Sensing of Medical Images Based on HSV Color Space. Sensors 2023;23(5):2616
    CrossRef
  130. Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi AK. Medical image segmentation using deep learning: A survey. IET Image Processing 2022;16(5):1243
    CrossRef
  131. ATASEVER S, AZGINOGLU N, TERZI DS, TERZI R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clinical Imaging 2023;94:18
    CrossRef
  132. Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing 2022;506:311
    CrossRef
  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
    CrossRef
  134. Loução R, Oros-Peusquens A, Langen K, Ferreira HA, Shah NJ. A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours. Frontiers in Oncology 2021;11
    CrossRef
  135. Amin J, Anjum MA, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  138. Litavec H, Basom RL. Incorporating a structural vulnerability framework into the forensic anthropology curriculum. Forensic Science International: Synergy 2023;6:100320
    CrossRef
  139. Yilmaz VS, Akdag M, Dalveren Y, Doruk RO, 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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  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
    CrossRef
  143. Swaraja K, Meenakshi K, Valiveti HB, 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
    CrossRef
  144. Wen B, Hu PJ, 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
    CrossRef
  145. Bhatele KR, Bhadauria SS. 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
    CrossRef
  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
    CrossRef
  147. Deepika MM, Raajan NR, Srinivasan A. Three dimensional reconstruction of brain tumor along with space occupying in lesions. Multimedia Tools and Applications 2022;81(9):12701
    CrossRef
  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
    CrossRef
  149. Alpar O, Dolezal R, Ryska P, Krejcar O. Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI. Pattern Recognition 2022;128:108675
    CrossRef
  150. Kumar A, Ghosal P, Kundu SS, 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
    CrossRef
  151. Natarajan S, Govindaraj V, Zhang Y, Murugan PR, 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
    CrossRef
  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
    CrossRef
  153. . Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 2023;10(1)
    CrossRef
  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
    CrossRef
  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)
    CrossRef
  156. Wani JA, Sofi TA, Sofi IA, Ganaie SA. The status of open access repositories in the field of technology: insights from OpenDOAR. Information Discovery and Delivery 2024;52(2):164
    CrossRef
  157. Preetha R, Priyadarsini MJP, Nisha JS. Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images. IEEE Access 2023;11:138549
    CrossRef
  158. Fan F, Ritschl L, Beister M, Biniazan R, Wagner F, Kreher B, Gottschalk TM, Kappler S, Maier A. Simulation‐driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans. Medical Physics 2023;
    CrossRef
  159. Lee J, Nishikawa RM. Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover. Breast Cancer Research 2024;26(1)
    CrossRef
  160. . Content-aware Nakagami morphing for incremental brain MRI. Knowledge-Based Systems 2024;291:111575
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.2930):

  1. Bousselham A, Bouattane O, Youssfi M, Raihani A. Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). 2020. Chapter 10:80
    CrossRef
  2. Agn M, Puonti O, Rosenschöld PMA, Law I, Van Leemput K. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2016. Chapter 15:168
    CrossRef
  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. 2016. Chapter 13:144
    CrossRef
  4. Bal A, Banerjee M, Sharma P, Chaki R. Advanced Computing and Systems for Security. 2020. Chapter 6:93
    CrossRef
  5. Chen X, Liew JH, Xiong W, Chui C, Ong S. Computer Vision – ECCV 2018. 2018. Chapter 40:674
    CrossRef
  6. Rezaei M, Yang H, Meinel C. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2019. Chapter 29:321
    CrossRef
  7. Pritanjli , Doegar A. Recent Trends in Communication and Intelligent Systems. 2020. Chapter 24:227
    CrossRef
  8. Bernardino G, Butakoff C, Nuñez-Garcia M, Sarvari SI, Rodriguez-Lopez M, Crispi F, González Ballester M, De Craene M, Bijnens B. Functional Imaging and Modelling of the Heart. 2017. Chapter 43:450
    CrossRef
  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. 2017. Chapter 11:99
    CrossRef
  10. Kalra M, Osadebey M, Bouguila N, Pedersen M, Fan W. Mixture Models and Applications. 2020. Chapter 11:235
    CrossRef
  11. Kao P, Chen JW, Manjunath BS. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2020. Chapter 4:32
    CrossRef
  12. Klíma O, Madeja R, Španel M, Čuta M, Zemčík P, Stoklásek P, Mizera A. Shape in Medical Imaging. 2018. Chapter 20:207
    CrossRef
  13. Jimenez DA, García HF, Álvarez AM, Orozco A. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 2018. Chapter 38:314
    CrossRef
  14. Kleesiek J, Biller A, Bartsch AJ, Ueltzhöffer K. Biomedical Engineering Systems and Technologies. 2015. Chapter 8:113
    CrossRef
  15. O’Connor J, Rutherford M, Hill J, Beverland D, Dunne N, Lennon A. Computer Methods in Biomechanics and Biomedical Engineering. 2018. Chapter 17:153
    CrossRef
  16. Tureckova A, Rodríguez-Sánchez AJ. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2019. Chapter 32:319
    CrossRef
  17. Mohan G, Subashini MM. Deep Learning and Parallel Computing Environment for Bioengineering Systems. 2019. :53
    CrossRef
  18. Saha R, Phophalia A, Mitra SK. Computer Vision, Graphics, and Image Processing. 2017. Chapter 12:133
    CrossRef
  19. Kong X, Sun G, Wu Q, Liu J, Lin F. Intelligent Information Processing IX. 2018. Chapter 35:346
    CrossRef
  20. Abler D, Rockne RC, Büchler P. New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering. 2019. Chapter 3:37
    CrossRef
  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. 2016. Chapter 18:184
    CrossRef
  22. Jimenez DA, García HF, Álvarez AM, Orozco A, Holguín G. Image Analysis and Recognition. 2018. Chapter 60:529
    CrossRef
  23. Pereira S, Pinto A, Alves V, Silva CA. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2016. Chapter 12:131
    CrossRef
  24. Shivhare SN, Kumar N. Proceedings of ICETIT 2019. 2020. Chapter 25:292
    CrossRef
  25. Pinheiro GR, Voltoline R, Bento M, Rittner L. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2019. Chapter 30:301
    CrossRef
  26. Fabijańska A, Vacavant A, Lebre M, Pavan ALM, de Pina DR, Abergel A, Chabrot P, Magnin B. Computer Vision and Graphics. 2018. Chapter 28:319
    CrossRef
  27. Bousselham A, Bouattane O, Youssfi M, Raihani A. Embedded Systems and Artificial Intelligence. 2020. Chapter 48:509
    CrossRef
  28. Liu P, Lee YZ, Aylward SR, Niethammer M. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. 2020. Chapter 67:688
    CrossRef
  29. de la Rosa E, Robben D, Sima DM, Kirschke JS, Menze B. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. 2020. Chapter 58:593
    CrossRef
  30. Madsen D, Aellen J, Morel-Forster A, Vetter T, Lüthi M. Shape in Medical Imaging. 2020. Chapter 3:30
    CrossRef
  31. Messaoudi R, Jaziri F, Vacavant A, Mtibaa A, Gargouri F. Pattern Recognition and Artificial Intelligence. 2020. Chapter 55:635
    CrossRef
  32. Madsen D, Morel-Forster A, Kahr P, Rahbani D, Vetter T, Lüthi M. Computer Vision – ECCV 2020. 2020. Chapter 17:281
    CrossRef
  33. Klug J, Leclerc G, Dirren E, Preti MG, Van De Ville D, Carrera E. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2021. Chapter 16:168
    CrossRef
  34. Shivhare SN, Kumar N. Progress in Advanced Computing and Intelligent Engineering. 2021. Chapter 3:29
    CrossRef
  35. Kaushal B, Patil MD, Birajdar GK. Handbook of Computational Intelligence in Biomedical Engineering and Healthcare. 2021. :315
    CrossRef
  36. . Advanced Healthcare Systems. 2022. :69
    CrossRef
  37. Kalaichelvi N, Kalaiselvi T, Somasundaram K. Applied Smart Health Care Informatics. 2022. :165
    CrossRef
  38. Sharma P, Goyal D, Tiwari N. Congress on Intelligent Systems. 2022. Chapter 27:381
    CrossRef
  39. . Deep Learning Technologies for the Sustainable Development Goals. 2023. Chapter 13:189
    CrossRef
  40. Shivhare SN, Kumar N. Proceedings of Academia-Industry Consortium for Data Science. 2022. Chapter 14:153
    CrossRef
  41. Alpar O, Krejcar O. Bioinformatics and Biomedical Engineering. 2023. Chapter 10:131
    CrossRef
  42. Alpar O, Krejcar O. Bioinformatics and Biomedical Engineering. 2023. Chapter 11:143
    CrossRef
  43. Halder R, Sharmin N. Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. 2024. Chapter 47:699
    CrossRef