Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation

• URL: http://arxiv.org/abs/2408.13639v1
• Date: Sat, 24 Aug 2024 17:55:02 GMT
• Title: Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation
• Authors: Jing Yuan, Tania Stathaki,
• Abstract summary: Scribble supervision has been widely used in medical image segmentation tasks to fasten network training. It often grapples with the challenge of accommodating varying scale targets, particularly in the context of medical images. We propose three novel methods to overcome these challenges, namely, 1) the cross-shape scribble annotation method; 2) the pseudo mask method based on cross shapes; and 3) the size-aware multi-branch method.
• Score: 8.209508547149952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scribble supervision, a common form of weakly supervised learning, involves annotating pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This technique has been widely used in medical image segmentation tasks to fasten network training. However, scribble supervision has limitations in terms of annotation consistency across samples and the availability of comprehensive groundtruth information. Additionally, it often grapples with the challenge of accommodating varying scale targets, particularly in the context of medical images. In this paper, we propose three novel methods to overcome these challenges, namely, 1) the cross-shape scribble annotation method; 2) the pseudo mask method based on cross shapes; and 3) the size-aware multi-branch method. The parameter and structure design are investigated in depth. Experimental results show that the proposed methods have achieved significant improvement in mDice scores across multiple polyp datasets. Notably, the combination of these methods outperforms the performance of state-of-the-art scribble supervision methods designed for medical image segmentation.

Related papers

• SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images [13.971120210536995]
  We introduce Scaling-up Mix with Multi-Class (SM2C) to improve the ability to learn semantic features within medical images. By diversifying the shape of the segmentation objects and enriching the semantic information within each sample, the SM2C demonstrates its potential. The proposed framework shows significant improvements over state-of-the-art counterparts.
  arXiv  Detail & Related papers  (2024-03-24T04:39:40Z)
• 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)
• 3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks [26.29122638813974]
  We propose a framework that can robustly learn from sparse annotation using the cross-teaching of both 3D and 2D networks. Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2023-07-30T15:26:17Z)
• ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding [5.425414924685109]
  ScribbleVC is a novel framework for scribble-supervised medical image segmentation. The proposed method combines a scribble-based approach with a segmentation network and a class-embedding module to produce accurate segmentation masks. We evaluate ScribbleVC on three benchmark datasets and compare it with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-30T13:38:52Z)
• SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings [12.79344668998054]
  We propose SwIPE (Segmentation with Implicit Patch Embeddings) to enable accurate local boundary delineation and global shape coherence. We show that SwIPE significantly improves over recent implicit approaches and outperforms state-of-the-art discrete methods with over 10x fewer parameters.
  arXiv  Detail & Related papers  (2023-07-23T20:55:11Z)
• Few Shot Medical Image Segmentation with Cross Attention Transformer [30.54965157877615]
  We propose a novel framework for few-shot medical image segmentation, termed CAT-Net. Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information. We validated the proposed method on three public datasets: Abd-CT, Abd-MRI, and Card-MRI.
  arXiv  Detail & Related papers  (2023-03-24T09:10:14Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• 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)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• Contrastive Rendering for Ultrasound Image Segmentation [59.23915581079123]
  The lack of sharp boundaries in US images remains an inherent challenge for segmentation. We propose a novel and effective framework to improve boundary estimation in US images. Our proposed method outperforms state-of-the-art methods and has the potential to be used in clinical practice.
  arXiv  Detail & Related papers  (2020-10-10T07:14:03Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.