VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image

TL;DR

The paper introduces VP-SFDA, a two-stage framework for adapting medical image models across domains without source data, improving segmentation performance through visual prompts and denoising.

Contribution

VP-SFDA introduces a novel two-stage SFUDA framework using input-specific visual prompts and batch normalization constraints to enhance domain adaptation in medical imaging.

Findings

VP-SFDA achieved a significant improvement in DICE score (0.658 to 0.773) in abdominal MRI to CT adaptation.

VP-LD and VP-DPL methods showed significant improvements over base algorithms in MRI to CT tasks.

Ablation studies confirmed the benefits of domain-specific patterns in VP-SFDA.

Abstract

Background: Source-free unsupervised domain adaptation (SFUDA) methods aim to address the challenge of domain shift while preserving data privacy. Existing SFUDA approaches construct reliable and confident pseudo-labels for target-domain data through denoising methods, thereby guiding the training of the target-domain model. The effectiveness of denoising approaches is influenced by the degree of domain gap between the source and target domains. A marked shift can cause the pseudo-labels to be unreliable, even after applying denoising. Methods: We propose a novel 2-stage framework for SFUDA called visual prompt source-free domain adaptation (VP-SFDA). We propose input-specific visual prompt in the first stage, prompting process, which bridges the target-domain data to source-domain distribution. Our method utilizes visual prompts and batch normalization constraint to enable the alignment model to learn domain-specific knowledge and align the target-domain data with the source-domain contribution. The second stage is the adaptation process, which aims at optimizing the segmentation model from the source domain to the target domain. This is accomplished through the denoising techniques, ultimately enhancing the performance. Results: Our study presents a comparative analysis of several SFUDA techniques in the VP-SFDA framework across 4 tasks: abdominal magnetic resonance imaging (MRI) to computed tomography (CT), abdominal CT to MRI, cardiac MRI to CT, and cardiac CT to MRI. Notably, in the abdominal MRI to CT adaptation task, the VP-OS method achieved a remarkable improvement, increasing the average DICE score from 0.658 to 0.773 (P
< 0.01) and reducing the average surface distance (ASD) from 3.489 to 2.961 (P
< 0.01). Similarly, the VP-LD and VP-DPL methods also showed significant improvements over their base algorithms in both abdominal and cardiac MRI to CT tasks. Conclusions: This paper proposes VP-SFDA, a novel 2-stage framework for SFUDA in medical imaging, which achieves superior performance through input-specific visual prompts and batch normalization constraint for domain adaptation, coupled with denoising methods for enhanced results. Comparative experiments on 4 medical SFUDA tasks demonstrate that VO-SFDA surpasses existing methods, with ablation studies confirming the benefits of domain-specific patterns.

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