Hounsfield Unit Ranges as Inductive Bias for Intra-Clinical Learning of Data-Efficient CT Segmentation Models

Automated tissue segmentation in medical imaging plays a critical role in clinical AI-assisted decision-making, and particularly in the assessment of body composition from CT scans. However, acquiring data and annotations of sufficient quality to train deep-learning models is expensive and timeconsuming. In this work, we propose a novel approach to improve data efficiency and model accuracy by leveraging domain knowledge about biologically relevant tissue-specific Hounsfield unit (HU) ranges as an inductive bias for learning. Specifically, we extend the input representation of deep learning-based segmentation models with binary masks indicating potential tissue types, where each binary mask is created from thresholds derived from medical literature. Our method not only enhances segmentation performance by up to 5% for intramuscular adipose tissue but surpasses the performance of the baseline model with 50% of the training data. Our easy-to-apply method thus improves data efficiency and facilitates the development and use of segmentation models in resource-constrained clinical settings.