Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation

• URL: http://arxiv.org/abs/2207.13240v3
• Date: Fri, 1 Sep 2023 17:08:02 GMT
• Title: Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation
• Authors: Xinrong Hu, Corey Wang, Yiyu Shi
• Abstract summary: CISFA builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. We use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image. We evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts.
• Score: 8.772764547425291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a novel framework CISFA (Contrastive Image synthesis and Self-supervised Feature Adaptation)that builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. Different from existing works, we use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image, which serves as shape constraints. Moreover, we notice that the generated images and input images share similar structural information but are in different modalities. As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space. Compared with existing works that rely on adversarial learning for feature adaptation, such a method enables the encoder to learn domain-independent features in a more explicit way. We extensively evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts. Experimental results show that the proposed framework not only outputs synthetic images with less distortion of organ shapes, but also outperforms state-of-the-art domain adaptation methods by a large margin.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers [0.0]
  We propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model. We show that our framework outperforms other cross-modality segmentation methods on the Multi-Modality Whole Heart Conditional Challenge.
  arXiv  Detail & Related papers  (2023-10-09T09:51:44Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis [63.845552349914186]
  We propose a style-guided instance-adaptive normalization (SIAN) to synthesize realistic color distributions and textures for different organs. The four phases work together and are integrated into a generative network to embed image semantics, style, and instance-level boundaries.
  arXiv  Detail & Related papers  (2022-09-02T16:45:46Z)
• CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis [4.898744396854313]
  We propose improvements over previous GAN-based medical image synthesis methods by learning the relationship between different anatomical labels. We use the synthetic images from our method to train networks for segmenting COVID-19 infected areas from lung CT images.
  arXiv  Detail & Related papers  (2021-06-15T07:21:24Z)
• Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization [0.43012765978447565]
  Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. These methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. We propose a segmentation-renormalized image translation framework to reduce inter-scanner harmonization while preserving anatomical layout.
  arXiv  Detail & Related papers  (2021-02-11T23:53:51Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• 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)

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.