论文标题
基于距离的损失，用于光声图像中光滑和连续的皮肤层分割
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
论文作者
论文摘要
栅格 - 扫描光声学介质（RSOM）是一种功能强大的非侵入性光学成像技术，用于功能，解剖和分子皮肤和组织分析。但是，此类图像的手册和自动分析都具有挑战性，因为RSOM图像的对比度非常低，信号与噪声比不良以及黑色素和血红蛋白吸收光谱之间的系统重叠。但是，表皮层的分割是许多下游医疗和诊断任务的关键步骤，例如血管分割或癌症进展的监测。我们提出了一种新颖的，特定于形状的损耗函数，该函数克服了不连续的分割并实现平滑的分割表面，同时保留了相同的体积骰子和iou。此外，我们通过血管分割的灵敏度来验证表皮分割。当提供表皮掩模作为船舶分割网络的附加信息时，我们发现船只细分任务的骰子骰子骰子的骰子有20美元。
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.
