DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning

• URL: http://arxiv.org/abs/2409.18340v1
• Date: Thu, 26 Sep 2024 23:30:40 GMT
• Title: DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning
• Authors: Hui Lin, Florian Schiffers, Santiago López-Tapia, Neda Tavakoli, Daniel Kim, Aggelos K. Katsaggelos,
• Abstract summary: Unsupervised domain adaptation (UDA) is essential for medical image segmentation, especially in cross-modality data scenarios. This paper presents DRL-STNet, a novel framework for cross-modality medical image segmentation. The proposed framework exhibits superior performance in abdominal organ segmentation on the FLARE challenge dataset.
• Score: 14.846510957922114
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation (UDA) is essential for medical image segmentation, especially in cross-modality data scenarios. UDA aims to transfer knowledge from a labeled source domain to an unlabeled target domain, thereby reducing the dependency on extensive manual annotations. This paper presents DRL-STNet, a novel framework for cross-modality medical image segmentation that leverages generative adversarial networks (GANs), disentangled representation learning (DRL), and self-training (ST). Our method leverages DRL within a GAN to translate images from the source to the target modality. Then, the segmentation model is initially trained with these translated images and corresponding source labels and then fine-tuned iteratively using a combination of synthetic and real images with pseudo-labels and real labels. The proposed framework exhibits superior performance in abdominal organ segmentation on the FLARE challenge dataset, surpassing state-of-the-art methods by 11.4% in the Dice similarity coefficient and by 13.1% in the Normalized Surface Dice metric, achieving scores of 74.21% and 80.69%, respectively. The average running time is 41 seconds, and the area under the GPU memory-time curve is 11,292 MB. These results indicate the potential of DRL-STNet for enhancing cross-modality medical image segmentation tasks.

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