Instance-Aware Deep Learning for Liver Metastasis Segmentation

Accurate segmentation of colorectal liver metastases (CRLM) from CT scans is essential for diagnosis and treatment planning in oncology. However, accurate lesion delineation remains a significant challenge due notably to instance imbalance. In this work, we propose an instance-aware segmentation model to enhance CRLM extraction, exploiting an instance-level loss function. Through careful evaluation, we analyze performance across diverse lesion morphologies and sizes, an aspect often overlooked for model validation. By leveraging a SwinUNETR-based architecture and three distinct datasets, we demonstrate improved instance detection ability, thereby contributing to better clinical decision-making in managing CRLM. A publicly-available implementation is provided: https://github.com/marwanabb/CRLM-instance/.