SALT: Introducing a framework for hierarchical segmentations in medical imaging using label trees.

ABSTRACT: Traditional segmentation networks treat anatomical structures as isolated elements, often neglecting their hierarchical relationships. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel method that leverages these hierarchical connections to improve segmentation efficiency and interpretability. SALT is a novel activation function that extends the softmax to arbitrary hierarchical label trees by modeling conditional probabilities along parent-child relations in imaging data. This enables anatomical hierarchies in CT imaging to be represented naturally, allowing segmentations to propagate from broader regions to detailed structures. Using the SAROS dataset from The Cancer Imaging Archive (TCIA), which comprises 900 body segmentations from 883 patients and was enhanced by

SUBMITTER: Becker SS 

PROVIDER: S-EPMC12717041 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature