A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images

TL;DR

This paper introduces a shape-specific loss function for optoacoustic skin layer segmentation that produces smooth, continuous surfaces and improves downstream vessel segmentation accuracy.

Contribution

The authors propose a novel distance-based loss function tailored for epidermis segmentation in RSOM images, enhancing surface smoothness without sacrificing volumetric accuracy.

Findings

20% improvement in vessel segmentation Dice score with epidermis mask

The loss function overcomes discontinuities in segmentation surfaces

Validation through vessel segmentation sensitivity analysis

Abstract

Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis. However, both the manual and the automated analysis of such images are challenging, because the RSOM images have very low contrast, poor signal to noise ratio, and systematic overlaps between the absorption spectra of melanin and hemoglobin. Nonetheless, the segmentation of the epidermis layer is a crucial step for many downstream medical and diagnostic tasks, such as vessel segmentation or monitoring of cancer progression. We propose a novel, shape-specific loss function that overcomes discontinuous segmentations and achieves smooth segmentation surfaces while preserving the same volumetric Dice and IoU. Further, we validate our epidermis segmentation through the sensitivity of vessel segmentation. We found a 20 $\%$ improvement in Dice for vessel segmentation tasks when the epidermis mask is provided as additional information to the vessel segmentation network.