Optimization with soft Dice can lead to a volumetric bias

Autor:	Bertels, Jeroen, Robben, David, Vandermeulen, Dirk, Suetens, Paul
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Zdroj:	LNCS 11992, Springer Nature Switzerland AG 2019
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-46640-4_9
Popis:	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. Comment: BrainLes Workshop - MICCAI 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.02278 Zobrazit plný text záznamu View this record from Arxiv