Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation

• URL: http://arxiv.org/abs/2109.05676v1
• Date: Mon, 13 Sep 2021 02:41:38 GMT
• Title: Domain and Content Adaptive Convolution for Domain Generalization in Medical Image Segmentation
• Authors: Shishuai Hu, Zehui Liao, Jianpeng Zhang, Yong Xia
• Abstract summary: We propose a multi-source domain generalization model, namely domain and content adaptive convolution (DCAC) In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image.
• Score: 20.633565211019853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model, namely domain and content adaptive convolution (DCAC), for medical image segmentation. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results indicate that the proposed DCAC model outperforms all competing methods on each segmentation task, and also demonstrate the effectiveness of the DAC and CAC modules.

Related papers

• Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation [49.5901368256326]
  We propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) in segmenting medical images. Our DAPSAM achieves state-of-the-art performance on two medical image segmentation tasks with different modalities.
  arXiv  Detail & Related papers  (2024-09-19T07:28:33Z)
• 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)
• CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation [38.44458104455557]
  We propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent.
  arXiv  Detail & Related papers  (2022-11-22T08:25:35Z)
• Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration [17.507951655445652]
  We present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration module. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images.
  arXiv  Detail & Related papers  (2022-08-08T03:56:20Z)
• Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
  We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
  arXiv  Detail & Related papers  (2022-07-04T07:05:06Z)
• Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation [12.863227646939563]
  We propose Contrastive Disentangle Domain (CDD) network for generalizable medical image segmentation. We first introduce a disentangle network to decompose medical images into an anatomical representation factor and a modality representation factor. We then propose a domain augmentation strategy that can randomly generate new domains for model generalization training.
  arXiv  Detail & Related papers  (2022-05-13T10:32:41Z)
• Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning [3.9562534927482704]
  We propose DCDA, a novel cross-modality unsupervised domain adaptation framework for tasks with large domain shifts. Our framework achieves Dice scores close to target-trained oracle both from OCTA to OCT and from OCT to OCTA, significantly outperforming other state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-13T07:03:16Z)
• 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)
• Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images [23.496487874821756]
  We propose a domain adaptation based self-correction model (DASC-Net) for COVID-19 infection segmentation on CT images. DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine results. Extensive experiments over three publicly available COVID-19 CT datasets demonstrate that DASC-Net consistently outperforms state-of-the-art segmentation, domain shift, and coronavirus infection segmentation methods.
  arXiv  Detail & Related papers  (2021-04-20T00:45:01Z)
• Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains [68.73614619875814]
  We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
  arXiv  Detail & Related papers  (2020-07-04T07:56:02Z)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)

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.