基于协作知识共享的点监督单细胞分割

IF 8.9 1区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS IEEE Transactions on Medical Imaging Pub Date : 2023-04-20 DOI:10.48550/arXiv.2304.10671
Ji Yu
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引用次数: 0

摘要

尽管深度学习方法具有优异的性能,但其缺点是需要大量经过良好注释的训练数据。作为回应,最近的文献已经看到了旨在减少注释负担的努力的扩散。本文主要研究单细胞分割模型的弱监督训练设置,其中唯一可用的训练标签是单个细胞的粗略位置。由于生物医学文献中广泛可用的细胞核反染色数据,可以通过编程推导细胞位置,因此具体问题具有实际意义。更普遍的兴趣是一种被提出的自我学习方法,称为协作知识共享,它与更知名的一致性学习方法相关,但又不同。该策略通过在主体模型和轻量级合作者模型之间共享知识来实现自我学习。重要的是,这两个模型在体系结构、能力和模型输出方面完全不同:在我们的例子中,主模型从对象检测的角度处理分割问题,而协作模型从语义分割的角度处理分割问题。我们通过在LIVECell(一个大型单细胞分割数据集的亮场图像)和A431(一个荧光图像数据集,其中位置标签是由细胞核反染色数据自动生成的)上进行实验来评估该策略的有效性。实现代码可从https://github.com/jiyuuchc/lacss获得。
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Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the only available training label is the rough locations of individual cells. The specific problem is of practical interest due to the widely available nuclei counter-stain data in biomedical literature, from which the cell locations can be derived programmatically. Of more general interest is a proposed self-learning method called collaborative knowledge sharing, which is related to but distinct from the more well-known consistency learning methods. This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model. Importantly, the two models are entirely different in their architectures, capacities, and model outputs: In our case, the principal model approaches the segmentation problem from an object-detection perspective, whereas the collaborator model a sematic segmentation perspective. We assessed the effectiveness of this strategy by conducting experiments on LIVECell, a large single-cell segmentation dataset of bright-field images, and on A431 dataset, a fluorescence image dataset in which the location labels are generated automatically from nuclei counter-stain data. Implementing code is available at https://github.com/jiyuuchc/lacss.
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来源期刊
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging 医学-成像科学与照相技术
CiteScore
21.80
自引率
5.70%
发文量
637
审稿时长
5.6 months
期刊介绍: The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.
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