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Citing this Article

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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"

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. 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
  2. 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
  3. 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
  4. Banerjee S, Mitra S, Uma Shankar B. Automated 3D segmentation of brain tumor using visual saliency. Information Sciences 2018;424:337
    CrossRef
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Khorram B, Yazdi M. A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation. Journal of Digital Imaging 2018;
    CrossRef
  11. Pinto A, Pereira S, Rasteiro D, Silva CA. Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognition 2018;82:105
    CrossRef
  12. 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
  13. 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
  14. 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
  15. 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;4:170117
    CrossRef
  16. Mitra S, Banerjee S, Hayashi Y, Najbauer J. Volumetric brain tumour detection from MRI using visual saliency. PLOS ONE 2017;12(11):e0187209
    CrossRef
  17. 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
  18. 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;4:170132
    CrossRef
  19. 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
  20. 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
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  21. 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
  22. Chang YH, Foley P, Azimi V, Borkar R, Lefman J. Primer for Image Informatics in Personalized Medicine. Procedia Engineering 2016;159:58
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  23. 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
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  24. 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
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  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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

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

:
  1. 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
  2. Jimenez DA, García HF, Álvarez AM, Orozco A, Holguín G. Image Analysis and Recognition. 2018. Chapter 60:529
    CrossRef
  3. Jimenez DA, García HF, Álvarez AM, Orozco A. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 2018. Chapter 38:314
    CrossRef
  4. Saha R, Phophalia A, Mitra SK. Computer Vision, Graphics, and Image Processing. 2017. Chapter 12:133
    CrossRef
  5. 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
  6. 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
  7. 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
  8. Pereira S, Pinto A, Alves V, Silva CA. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2016. Chapter 12:131
    CrossRef
  9. 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
  10. 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