RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation

• URL: http://arxiv.org/abs/2508.02844v1
• Date: Mon, 04 Aug 2025 19:14:30 GMT
• Title: RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation
• Authors: Anghong Du, Nay Aung, Theodoros N. Arvanitis, Stefan K. Piechnik, Joao A C Lima, Steffen E. Petersen, Le Zhang,
• Abstract summary: High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks.<n>We propose a novel coarse-to-fine segmentation framework that relies entirely on coarse-level annotations.
• Score: 2.608565452856053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks, but obtaining such annotations is costly and requires medical expertise. To address this challenge, we propose a novel coarse-to-fine segmentation framework that relies entirely on coarse-level annotations, encompassing both target and complementary drawings, despite their inherent noise. The framework works by introducing transition matrices in order to model the inaccurate and incomplete regions in the coarse annotations. By jointly training on multiple sets of coarse annotations, it progressively refines the network's outputs and infers the true segmentation distribution, achieving a robust approximation of precise labels through matrix-based modeling. To validate the flexibility and effectiveness of the proposed method, we demonstrate the results on two public cardiac imaging datasets, ACDC and MSCMRseg, and further evaluate its performance on the UK Biobank dataset. Experimental results indicate that our approach surpasses the state-of-the-art weakly supervised methods and closely matches the fully supervised approach.

Related papers

• TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation [4.034121387622003]
  We propose TAB Net, a novel weakly-supervised medical image segmentation framework.<n>It consists of the triplet augmentation self-recovery (TAS) module and the boundary-aware pseudo-label supervision (BAP) module.<n>We show that TAB Net significantly outperforms state-of-the-art methods for scribble-based weakly supervised segmentation.
  arXiv  Detail & Related papers  (2025-07-03T07:50:00Z)
• Semi-Supervised Medical Image Segmentation via Dual Networks [1.904929457002693]
  We propose an innovative semi-supervised 3D medical image segmentation method to reduce the dependency on large, expert-labeled datasets.<n>We introduce a dual-network architecture to address the limitations of existing methods in using contextual information.<n> Experiments on clinical magnetic resonance imaging demonstrate that our approach outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-05-23T09:59:26Z)
• Mind the Context: Attention-Guided Weak-to-Strong Consistency for Enhanced Semi-Supervised Medical Image Segmentation [13.679842517127248]
  This paper introduces a simple yet efficient semi-supervised learning framework named Attention-Guided weak-to-strong Consistency Match (AIGCMatch)<n>The AIGCMatch framework incorporates attention-guided perturbation strategies at both the image and feature levels to achieve weak-to-strong consistency regularization.<n>Our method achieved a 90.4% Dice score in the 7-case scenario on the ACDC dataset, surpassing the state-of-the-art methods and demonstrating its potential and efficacy in clinical settings.
  arXiv  Detail & Related papers  (2024-10-16T10:04:22Z)
• A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation [1.9639956888747314]
  We study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model's segmentation.
  arXiv  Detail & Related papers  (2024-10-11T04:19:48Z)
• Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations [49.33388736227072]
  We propose a semi- and weakly-supervised learning framework for mass segmentation. We use limited strongly-labeled samples and sufficient weakly-labeled samples to achieve satisfactory performance. Experiments on CBIS-DDSM and INbreast datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-03-14T12:05:25Z)
• 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)
• Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation [10.18427897663732]
  Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis. This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net) Experiments on LA and Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art methods for semi-supervised segmentation.
  arXiv  Detail & Related papers  (2023-05-25T16:23:39Z)
• 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)
• 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)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning [66.36706284671291]
  We propose a data-driven framework supervised by only image-level labels to support unbiased lesion localisation. The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder.
  arXiv  Detail & Related papers  (2021-03-01T06:05:49Z)
• 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)

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.