Abstract
Segmentation is a fundamental task in medical image analysis. The clinical interest is often to measure the volume of a structure. To evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using metrics such as the Dice score. Recent segmentation methods based on convolutional neural networks use a differentiable surrogate of the Dice score, such as soft Dice, explicitly as the loss function during the learning phase. Even though this approach leads to improved Dice scores, we find that, both theoretically and empirically on four medical tasks, it can introduce a volumetric bias for tasks with high inherent uncertainty. As such, this may limit the method’s clinical applicability.
J. Bertels and D. Robben—Contributed equally to this work.
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Acknowledgements
J.B. is part of NEXIS [1], a project that has received funding from the European Union’s Horizon 2020 Research and Innovations Programme (Grant Agreement #780026). D.R. is supported by an innovation mandate of Flanders Innovation and Entrepreneurship (VLAIO).
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Bertels, J., Robben, D., Vandermeulen, D., Suetens, P. (2020). Optimization with Soft Dice Can Lead to a Volumetric Bias. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_9
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