Across the four cohorts and two model initializations, AtrousLoRA produced Dice scores between 1.14 and 4.50 percentage points higher than standard LoRA. These findings apply to the evaluated datasets, prompt conditions and initialization settings rather than to unrestricted automatic segmentation. Accurate aortic segmentation involves delineating vessels with different calibers and irregular boundaries.

The researchers noted that full encoder fine-tuning of SAM updates many parameters, while conventional LoRA does not aggregate image context across different receptive fields within its task-specific update. VesselSAM was developed to accommodate both considerations by modifying the query and value projections in each image-encoder block of the base ViT-B vision transformer. At the technical level, the researchers inserted a multi-branch Atrous Attention Module into each rank-4 residual path associated with the selected query and value projections.

This architecture allowed the low-rank adaptation to process image context at multiple receptive-field scales while retaining a relatively small trainable parameter set. VesselSAM was initialized from MedSAM for the binary segmentation task, and the comparative evaluation considered both SAM and MedSAM initialization. The Aortic Vessel Tree cohorts were Dongyang, Rider and KiTS.

Tight bounding boxes derived from the ground-truth annotations were supplied at validation and test time, making the reported scores specific to a prompted segmentation setting rather than a fully automatic detection task. VesselSAM attained a slice-averaged Dice similarity coefficient of 93.50% on the Dongyang cohort, 93.25% on Rider and 93.02% on KiTS. The corresponding result for the Type-B Aortic Dissection dataset was 93.26%.

The consistent performance across these four evaluated cohorts shows that the reported numerical results were not confined to a single named dataset, although the evidence does not establish how the method would perform in other clinical settings or without ground-truth-derived prompts. The model updated 6.8 million parameters, equivalent to 7.2% of its total parameters, underscoring the parameter-efficient nature of the adaptation. The study’s central finding is therefore twofold: AtrousLoRA outperformed standard LoRA under the specified evaluation, and VesselSAM achieved high overlap scores for binary aortic vessel segmentation without full encoder fine-tuning.

Those results support the approach as a prompt-dependent adaptation of pretrained segmentation models, not as evidence of clinical benefit or clinical deployment. Overall, the research presents AtrousLoRA as a way to incorporate multiscale spatial processing into low-rank adaptation for aortic vessel segmentation. The reported gains over standard LoRA were measured across four cohorts and two initializations, with the strongest absolute DSC reaching 93.50%.

Important limits remain: the system relied on tight ground-truth-derived box prompts, and the supplied evidence does not demonstrate performance for prompt-free segmentation, unseen populations, or routine clinical use.

Subject of Research: Prompted parameter-efficient adaptation of SAM for aortic vessel segmentation in CT angiography

Article Title: VesselSAM: a parameter-efficient adaptation of SAM to aortic vessel segmentation with AtrousLoRA

Article References: Zhao, X., Zhuang, P., Zhang, Z., Iltaf, A., Ahmed, R. M., & Li, B. (2026). VesselSAM: a parameter-efficient adaptation of SAM to aortic vessel segmentation with AtrousLoRA. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02911-1

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02911-1

Keywords: VesselSAM, AtrousLoRA, aortic segmentation, CT angiography, Segment Anything Model, MedSAM, low-rank adaptation, Dice coefficient, vision transformer, medical imaging, parameter-efficient, adaptation