Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

• URL: http://arxiv.org/abs/2208.08605v1
• Date: Thu, 18 Aug 2022 02:54:04 GMT
• Title: Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures
• Authors: Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang
• Abstract summary: We propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation. It adapts a model to segment similar structures in a target domain. It requires only limited annotations in the target domain.
• Score: 21.54339967787734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised Learning (SSL) methods are promising to reduce the requirement of annotations, but their performance is still limited when the dataset size and the number of annotated images are small. Leveraging existing annotated datasets with similar anatomical structures to assist training has a potential for improving the model's performance. However, it is further challenged by the cross-anatomy domain shift due to the different appearance and even imaging modalities from the target structure. To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain. We use Domain-Specific Batch Normalization (DSBN) to individually normalize feature maps for the two anatomical domains, and propose a cross-domain contrastive learning strategy to encourage extracting domain invariant features. They are integrated into a Self-Ensembling Mean-Teacher (SE-MT) framework to exploit unlabeled target domain images with a prediction consistency constraint. Extensive experiments show that our CS-CADA is able to solve the challenging cross-anatomy domain shift problem, achieving accurate segmentation of coronary arteries in X-ray images with the help of retinal vessel images and cardiac MR images with the help of fundus images, respectively, given only a small number of annotations in the target domain.

Related papers

• Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images [13.03504366061946]
  We propose a method named Adaptive Feature-fusion Neural Network (AFNN) for glaucoma segmentation on unseen domains. The domain adaptor helps the pretrained-model fast adapt from other image domains to the medical fundus image domain. Our proposed method achieves a competitive performance over existing fundus segmentation methods on four public glaucoma datasets.
  arXiv  Detail & Related papers  (2024-04-02T16:30:12Z)
• SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation [100.86339246424541]
  We propose a novel Structure-Modal Constrained (SMC) UDA framework based on a discriminative paradigm and introduce edge structure as a bridge between domains. With the structure-constrained self-learning and progressive ROI, our methods segment the kidney by locating the 3D spatial structure of the edge. experiments show that our proposed SMC-UDA has a strong generalization and outperforms generative UDA methods.
  arXiv  Detail & Related papers  (2023-06-14T02:57:23Z)
• Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation [49.59567529191423]
  We propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D.
  arXiv  Detail & Related papers  (2022-03-07T19:02:26Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation [10.417009344120917]
  We present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. We validated our method on two cardiac datasets by adapting from the annotated source domain to the unannotated target domain.
  arXiv  Detail & Related papers  (2021-03-15T08:59:44Z)
• Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation [9.659642285903418]
  Cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
  arXiv  Detail & Related papers  (2021-03-05T16:22:31Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets [96.92018649136217]
  We present a novel Domain-oriented Feature Embedding (DoFE) framework to improve the generalization ability of CNNs on unseen target domains. Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains. Our framework generates satisfying segmentation results on unseen datasets and surpasses other domain generalization and network regularization methods.
  arXiv  Detail & Related papers  (2020-10-13T07:28:39Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)
• Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns [6.298270929323396]
  We propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains. We enforce architectures to be adaptive to new data by rejecting improbable segmentation patterns and implicitly learning through semantic and boundary information. We demonstrate that recalibrating the deep networks on a few unlabeled images from the target domain improves the segmentation accuracy significantly.
  arXiv  Detail & Related papers  (2020-01-25T13:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.