Learning Semi-Supervised Medical Image Segmentation from Spatial Registration

• URL: http://arxiv.org/abs/2409.10422v1
• Date: Mon, 16 Sep 2024 15:52:41 GMT
• Title: Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
• Authors: Qianying Liu, Paul Henderson, Xiao Gu, Hang Dai, Fani Deligianni,
• Abstract summary: CCT-R is a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules. Experimental results demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings.
• Score: 20.08775594086033
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information -- spatial registration transforms between image volumes. To address this, we propose CCT-R, a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs, providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings, with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration.

Related papers

• SAMReg: SAM-enabled Image Registration with ROI-based Correspondence [12.163299991979574]
  This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs) for medical image registration. We develop a new registration algorithm SAMReg, which does not require any training (or training data), gradient-based fine-tuning or prompt engineering. The proposed methods outperform both intensity-based iterative algorithms and DDF-predicting learning-based networks across tested metrics.
  arXiv  Detail & Related papers  (2024-10-17T23:23:48Z)
• 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)
• AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation [24.902447478719303]
  We propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER) Specifically, we implement a novel dual consistency constraint to ensure anatomy-aligned registration that lessens registration errors. We also develop an adversarial training strategy to augment the atlas image, which ensures both generation diversity and segmentation robustness.
  arXiv  Detail & Related papers  (2023-09-02T16:06:50Z)
• Structure-aware registration network for liver DCE-CT images [50.28546654316009]
  We propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Our proposed method can achieve higher registration accuracy and preserve anatomical structure more effectively than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-08T14:08:56Z)
• RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation [26.933651788004475]
  We propose a novel semi-supervised segmentation method named Rectified Contrastive Pseudo Supervision (RCPS) RCPS combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-13T12:03:58Z)
• 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)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation [46.678279106837294]
  We propose a cross-level constrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance.
  arXiv  Detail & Related papers  (2022-02-08T15:12:11Z)
• 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)
• RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels [9.81466618834274]
  We propose a new and efficient method for medical image segmentation under noisy labels. The method operates under a deep learning paradigm, incorporating four novel contributions. Experimental results are illustrated on a publicly available benchmark database of spine CTs.
  arXiv  Detail & Related papers  (2020-09-27T15:32:50Z)

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.