HELPNet: Hierarchical perturbations consistency and entropy-guided ensemble for scribble supervised medical image segmentation

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiao Zhang , Shaoxuan Wu , Peilin Zhang , Zhuo Jin , Xiaosong Xiong , Qirong Bu , Jingkun Chen , Jun Feng
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引用次数: 0

Abstract

Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize reliance on detailed annotations. Scribble annotations offer a more cost-effective alternative, significantly reducing the expenses associated with full annotations. However, scribble annotations offer limited and imprecise information, failing to capture the detailed structural and boundary characteristics necessary for accurate organ delineation. To address these challenges, we propose HELPNet, a novel scribble-based weakly supervised segmentation framework, designed to bridge the gap between annotation efficiency and segmentation performance. HELPNet integrates three modules. The Hierarchical perturbations consistency (HPC) module enhances feature learning by employing density-controlled jigsaw perturbations across global, local, and focal views, enabling robust modeling of multi-scale structural representations. Building on this, the Entropy-guided pseudo-label (EGPL) module evaluates the confidence of segmentation predictions using entropy, generating high-quality pseudo-labels. Finally, the Structural prior refinement (SPR) module integrates connectivity analysis and image boundary prior to refine pseudo-label quality and enhance supervision. Experimental results on three public datasets ACDC, MSCMRseg, and CHAOS show that HELPNet significantly outperforms state-of-the-art methods for scribble-based weakly supervised segmentation and achieves performance comparable to fully supervised methods. The code is available at https://github.com/IPMI-NWU/HELPNet.
用于潦草监督医学图像分割的层次摄动一致性和熵引导集成
为医学图像分割创建完全注释的标签是非常耗时和昂贵的,这强调了创新方法的必要性,以最大限度地减少对详细注释的依赖。潦草注释提供了一种更具成本效益的替代方案,显著降低了与完整注释相关的费用。然而,潦草的注释提供了有限和不精确的信息,未能捕捉到准确描绘器官所必需的详细结构和边界特征。为了解决这些挑战,我们提出了一种新的基于潦草的弱监督分割框架HELPNet,旨在弥合标注效率和分割性能之间的差距。HELPNet集成了三个模块。层次扰动一致性(HPC)模块通过在全局、局部和焦点视图中使用密度控制的拼图扰动来增强特征学习,从而实现多尺度结构表示的鲁棒建模。在此基础上,熵引导伪标签(EGPL)模块使用熵评估分割预测的置信度,生成高质量的伪标签。最后,结构先验改进(SPR)模块将连通性分析和图像边界先验相结合,提高伪标签质量,增强监管。在ACDC、MSCMRseg和CHAOS三个公共数据集上的实验结果表明,HELPNet在基于潦草的弱监督分割方面明显优于最先进的方法,并且达到了与完全监督方法相当的性能。代码可在https://github.com/IPMI-NWU/HELPNet上获得。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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