Related papers: Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

URL: http://arxiv.org/abs/2404.08951v1
Date: Sat, 13 Apr 2024 10:15:51 GMT
Title: Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
Authors: Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao,
Abstract summary: Both limited annotation and domain shift are prevalent challenges in medical image segmentation. We introduce Mixed Domain Semi-supervised medical image components (MiDSS) Our method achieves a notable 13.57% improvement in Dice score on Prostate dataset, as demonstrated on three public datasets.
Score: 36.45117307751509
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However, the coexistence of limited annotation and domain shift is quite common, which motivates us to introduce a novel and challenging scenario: Mixed Domain Semi-supervised medical image Segmentation (MiDSS). In this scenario, we handle data from multiple medical centers, with limited annotations available for a single domain and a large amount of unlabeled data from multiple domains. We found that the key to solving the problem lies in how to generate reliable pseudo labels for the unlabeled data in the presence of domain shift with labeled data. To tackle this issue, we employ Unified Copy-Paste (UCP) between images to construct intermediate domains, facilitating the knowledge transfer from the domain of labeled data to the domains of unlabeled data. To fully utilize the information within the intermediate domain, we propose a symmetric Guidance training strategy (SymGD), which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples. Subsequently, we introduce a Training Process aware Random Amplitude MixUp (TP-RAM) to progressively incorporate style-transition components into intermediate samples. Compared with existing state-of-the-art approaches, our method achieves a notable 13.57% improvement in Dice score on Prostate dataset, as demonstrated on three public datasets. Our code is available at https://github.com/MQinghe/MiDSS .