VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with LoRA and Atrous Attention
Journal:
arXiv
Published Date:
Feb 25, 2025
Abstract
Medical image segmentation is crucial for clinical diagnosis and treatment
planning, especially when dealing with complex anatomical structures such as
vessels. However, accurately segmenting vessels remains challenging due to
their small size, intricate edge structures, and susceptibility to artifacts
and imaging noise. In this work, we propose VesselSAM, an enhanced version of
the Segment Anything Model (SAM), specifically tailored for aortic vessel
segmentation. VesselSAM incorporates AtrousLoRA, a novel module integrating
Atrous Attention and Low-Rank Adaptation (LoRA), to enhance segmentation
performance. Atrous Attention enables the model to capture multi-scale
contextual information, preserving both fine-grained local details and broader
global context. Additionally, LoRA facilitates efficient fine-tuning of the
frozen SAM image encoder, reducing the number of trainable parameters and
thereby enhancing computational efficiency. We evaluate VesselSAM using two
challenging datasets: the Aortic Vessel Tree (AVT) dataset and the Type-B
Aortic Dissection (TBAD) dataset. VesselSAM achieves state-of-the-art
performance, attaining DSC scores of 93.50\%, 93.25\%, 93.02\%, and 93.26\%
across multi-center datasets. Our results demonstrate that VesselSAM delivers
high segmentation accuracy while significantly reducing computational overhead
compared to existing large-scale models. This development paves the way for
enhanced AI-based aortic vessel segmentation in clinical environments. The code
and models will be released at https://github.com/Adnan-CAS/AtrousLora.
