Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
By O. Commowick, Audrey Istace, Michael Kain, Baptiste Laurent, F. Leray, Mathieu Simon, Sorina Pop, P. Girard, R. Ameli, J. Ferré and 35 more. Scientific Reports, 2018
298 citations recorded by the source corpus.
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
Subfields: Epidemiology, Microbiology, Pathology and Forensic Medicine
View Paper: https://www.semanticscholar.org/paper/63275cef678cb6e280efcf11b527f594335c02c8
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In Epidemiology
- Gary S. Collins
- Karel G. M. Moons
- Geert Jan Biessels
- David Moher
- Douglas G. Altman
- Johannes B Reitsma
In Microbiology
In Pathology and Forensic Medicine
Citations in the corpus
- Metrics Reloaded: Recommendations for image analysis validation (2022, background): “48) • Cell nuclei instance segmentation in time-lapse light microscopy with a subsequent goal of cell tracking [87] • MS lesion segmentation in multi-modal brain MRI images [30, 53]”
- Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge (2019, background): “images, such as the multiple sclerosis (MS) lesions [11], [12], tumor [13], or tissue [14] segmentation challenges.”
- Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, methodology): “The increase in DSC is also interesting and less expected since small lesions account for a small fraction of the entire lesion volume, which greatly affects the DSC.”