A Clinical Knowledge-Driven Fine-Tuning Strategy for Applying Foundation Model to Fully Automatic Acute Ischemic Stroke Lesion Segmentation on Non-Contrast CT Scans

Abstract

Segmentation of lesions in Acute Ischemic Stroke (AIS) patients on Non-Contrast CT (NCCT) scans is pivotal for expedited diagnosis and effective treatment planning. The subtle and 4.5-hour golden treatment window characteristic of AIS lesions on NCCT makes fully automated segmentation more preferred. We introduce AISCT-SAM, a fine-tuned Segment Anything Model (SAM), for this task. Specifically, we: 1) integrate an adaptive gate control mechanism with Adapter and Low-Rank Adaptation to comprehensively fine-tune the encoder of the foundational SAM model; 2) incorporate a depth adapter to extract richer 3D contextual information, enhancing lesion continuity; 3) propose a self-prompt generator that leverages bilateral hemisphere differences to generate prompts for the mask decoder, enabling fully automatic segmentation. It is evaluated on both public and private datasets, with results from internal and external validations showing state-of-the-art performance. Additionally, volumetric analysis indicates that AISCT-SAM can provide valuable reference for diagnostic and treatment decisions. Our data and code are publicly available at: https://github.com/GitHub-TXZ/AISCT-SAM.