Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI

TL;DR

This paper introduces the Contour Dice loss for improved segmentation of fetal MRI structures with fuzzy and complex boundaries, demonstrating its effectiveness in placenta and fetal brain segmentation tasks.

Contribution

It proposes a novel Contour Dice loss formulation and compares its performance with existing boundary losses in fetal MRI segmentation.

Findings

Contour Dice loss improves placenta segmentation accuracy.

Combined Dice and Cross-Entropy loss performs best for fetal brain segmentation.

Contour Dice loss outperforms other boundary losses in specific scenarios.

Abstract

Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe a new formulation of the loss akin to the Contour Dice metric. The combination of the Dice loss and the Contour Dice yielded the best performance for placenta segmentation. For fetal brain segmentation, the best performing loss was the combined Dice with Cross-Entropy loss followed by the Dice with Contour Dice loss, which performed better than other boundary losses.