A-ADAPT: Adaptive Intracranial Artery Segmentation with Morphology-Guided Prompts and Difficulty-Aware Learning

Zhiwei Tan, Xin Wang, Meng Wang, Zixuan Liu, Yin Guo, Jiamin Xia, Niranjan Balu, Linda Shapiro, Chun Yuan, Mahmud Mossa-Basha

Keywords: Intracranial artery segmentation, Uncertainty-guided refinement;SAM

TL;DR: CTA, MRA, Intracranial artery segmentation, automatic prompt, SAM

Abstract:

Accurate segmentation of intracranial arteries in CTA and MRA is essential for cerebrovascular analysis but remains challenging due to fine-scale artery morphology, modality-dependent appearance, and frequent structural discontinuities. Existing CNN or Transformer based models struggle to generalize across modalities, while SAM-based methods rely heavily on manually provided prompts and often fail to preserve thin or low-contrast arteries.
We propose A-ADAPT, an adaptive intracranial artery segmentation framework that enhances SAM with modality-aware representation learning, automatic morphology-guided prompting, and difficulty-aware optimization. First, a Cross-Modality Task Adapter (CMTA) aligns CTA and MRA feature distributions while preserving shared vascular characteristics. The Frequency Adapter (FA) and the Tubular Morphology Adapter(TMA) work together to refine artery representation by enhancing structural detail and highlighting the continuity of tubular anatomy. To eliminate dependence on manual prompts, we introduce an Automatic Directional Morphology Prompt Encoder (AutoDM-Prompt), which generates artery-aware prompts directly from the input image. Additionally, a difficulty-aware loss dynamically upweights uncertain or discontinuity-prone regions, enabling the model to better recover small branches and reduce false positives.
Experiments on CTA and MRA datasets show that A-ADAPT achieves higher accuracy, and better structural continuity compared to several state-of-the-art methods. The code will be available at Github once acceptance.

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Radiology

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Submission Number: 118