Multi-Faceted Consistency learning with active cross-labeling for barely-supervised 3D medical image segmentation

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xinyao Wu , Zhe Xu , Raymond Kai-yu Tong
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引用次数: 0

Abstract

Deep learning-driven 3D medical image segmentation generally necessitates dense voxel-wise annotations, which are expensive and labor-intensive to acquire. Cross-annotation, which labels only a few orthogonal slices per scan, has recently emerged as a cost-effective alternative that better preserves the shape and precise boundaries of the 3D object than traditional weak labeling methods such as bounding boxes and scribbles. However, learning from such sparse labels, referred to as barely-supervised learning (BSL), remains challenging due to less fine-grained object perception, less compact class features and inferior generalizability. To tackle these challenges and foster collaboration between model training and human expertise, we propose a Multi-Faceted ConSistency learning (MF-ConS) framework with a Diversity and Uncertainty Sampling-based Active Learning (DUS-AL) strategy, specifically designed for the active BSL scenario. This framework combines a cross-annotation BSL strategy, where only three orthogonal slices are labeled per scan, with an AL paradigm guided by DUS to direct human-in-the-loop annotation toward the most informative volumes under a fixed budget. Built upon a teacher–student architecture, MF-ConS integrates three complementary consistency regularization modules: (i) neighbor-informed object prediction consistency for advancing fine-grained object perception by encouraging the student model to infer complete segmentation from masked inputs; (ii) prototype-driven consistency, which enhances intra-class compactness and discriminativeness by aligning latent feature and decision spaces using fused prototypes; and (iii) stability constraint that promotes model robustness against input perturbations. Extensive experiments on three benchmark datasets demonstrate that MF-ConS (DUS-AL) consistently outperforms state-of-the-art methods under extremely limited annotation.
基于主动交叉标记的多面一致性学习在无监督三维医学图像分割中的应用
深度学习驱动的三维医学图像分割通常需要密集的体素注释,这是昂贵和劳动密集型的获取。交叉注释,每次扫描只标记几个正交切片,最近作为一种经济有效的替代方法出现,它比传统的弱标记方法(如边界框和涂鸦)更好地保留了3D对象的形状和精确边界。然而,从这种稀疏标签中学习,被称为无监督学习(BSL),由于缺乏细粒度的对象感知,不太紧凑的类特征和较差的泛化性,仍然具有挑战性。为了应对这些挑战并促进模型训练与人类专业知识之间的合作,我们提出了一个多面一致性学习(MF-ConS)框架,该框架具有基于多样性和不确定性采样的主动学习(DUS-AL)策略,专门为主动车挂语场景设计。该框架结合了交叉注释BSL策略,其中每次扫描仅标记三个正交切片,以及由DUS指导的人工智能范式,以在固定预算下将人在环注释指向最具信息量的卷。基于师生架构,MF-ConS集成了三个互补的一致性正则化模块:(i)通过鼓励学生模型从屏蔽输入推断完整分割,从而提高细粒度对象感知的邻居通知对象预测一致性;(ii)原型驱动一致性,通过使用融合原型对齐潜在特征和决策空间,增强类内紧密性和判别性;(iii)稳定性约束,提高模型对输入扰动的鲁棒性。在三个基准数据集上进行的大量实验表明,MF-ConS (DUS-AL)在极其有限的注释下始终优于最先进的方法。
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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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