A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings

TL;DR

This paper introduces a 3D SAM-based progressive prompting framework for multi-task segmentation of radiotherapy-induced injuries in limited-data settings, utilizing a new dataset and multiple prompts for improved accuracy.

Contribution

The study presents a novel 3D SAM-based framework with progressive prompts and a small-target focus loss for effective multi-task segmentation in scarce data scenarios.

Findings

Achieves reliable segmentation across diverse injury types

Outperforms state-of-the-art methods in experiments

Demonstrates effectiveness on a new dedicated dataset

Abstract

Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images could facilitate disease assessment, treatment planning, and longitudinal monitoring. However, automatic segmentation of these lesions remains largely unexplored because of limited voxel-level annotations and substantial heterogeneity across injury types, lesion size, and imaging modality. To address this gap, we curate a dedicated head-and-neck radiotherapy-induced normal tissue injury dataset covering three manifestations: osteoradionecrosis (ORN), cerebral edema (CE), and cerebral radiation necrosis (CRN). We further propose a 3D SAM-based progressive prompting framework for multi-task segmentation in limited-data settings. The framework progressively incorporates three complementary prompts: text prompts for task-aware adaptation, dose-guided box prompts for coarse localization, and click prompts for iterative refinement. A small-target focus loss is introduced to improve local prediction and boundary delineation for small and sparse lesions. Experiments on ORN, CE, and CRN demonstrate that the proposed method achieves reliable segmentation performance across diverse injury types and outperforms state-of-the-art methods.