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

By M. Kistler, S. Bonaretti, Marcel Pfahrer, Roman Niklaus, P. Büchler. Journal of Medical Internet Research, 2013

297 citations recorded by the source corpus.

Background Statistical shape models are widely used in biomedical research. They are routinely implemented for automatic image segmentation or object identification in medical images. In these fields, however, the acquisition of the large training datasets, required to develop these models, is usually a time-consuming process. Even after this effort, the collections of datasets are often lost or mishandled resulting in replication of work. Objective To solve these problems, the Virtual Skeleton Database (VSD) is proposed as a centralized storage system where the data necessary to build statistical shape models can be stored and shared. Methods The VSD provides an online repository system tailored to the needs of the medical research community. The processing of the most common image file types, a statistical shape model framework, and an ontology-based search provide the generic tools to store, exchange, and retrieve digital medical datasets. The hosted data are accessible to the community, and collaborative research catalyzes their productivity. Results To illustrate the need for an online repository for medical research, three exemplary projects of the VSD are presented: (1) an international collaboration to achieve improvement in cochlear surgery and implant optimization, (2) a population-based analysis of femoral fracture risk between genders, and (3) an online application developed for the evaluation and comparison of the segmentation of brain tumors. Conclusions The VSD is a novel system for scientific collaboration for the medical image community with a data-centric concept and semantically driven search option for anatomical structures. The repository has been proven to be a useful tool for collaborative model building, as a resource for biomechanical population studies, or to enhance segmentation algorithms.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Orthopedics and Sports Medicine

View Paper: https://www.semanticscholar.org/paper/80acb321ba193213a8ea3e94b74aad5e10e4b4b5

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In Biomedical Engineering

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In Orthopedics and Sports Medicine

Victor Alves

Citations in the corpus

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “We used two different platforms: the Virtual Skeleton Database (VSD), hosted at the University of Bern, and the Multimedia Digital Archiving System (MIDAS), hosted at Kitware [80].”
CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation (2020, background): “Similarly, Shape 2014 and 2015 [15] challenges focused on liver segmentation from CT data.”
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation (2018, methodology): “Semantic segmentation is an active research area where DCNNs are used to classify each pixel in the image individually, which is fueled by different challenging datasets in the fields of computer vision and medical imaging [23, 24, and 25].”
Brain tumor detection and classification using machine learning: a comprehensive survey (2021, background): “BRATS are the most challenging MRI datasets [55–57].”
Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, background): “Brain tumor Segmentation MRI BraTS [78] Zhao et al.”
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images (2016, methodology): “The proposed method was validated on the BRATS 2013 and 2015 databases 3 [5], [46].”
Recurrent residual U-Net for medical image segmentation (2019, methodology): “26 Before the DL revolution, the traditional machine-learning approach mostly relied on hand-engineered features that were used for classifying pixels independently.”
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, methodology): “4) Brain Tumor: The dataset is provided by BraTS 2013 [32], [34].”
Computer Vision, Graphics, and Image Processing (2016, methodology): “Incorrect matches are eliminated by locally verifying the transformation between fragments as in [12] or by checking the global compatibility between matches as in [18].”