[类器官图像深度感知技术综述]。

Q4 Medicine
Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo
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引用次数: 0

摘要

类器官是一种体外模型,可以模拟体内组织的复杂结构和功能。通过类器官图像分析实现了分类、筛选和轨迹识别等功能,但仍存在识别分类和细胞追踪准确率低等问题。深度学习算法和类器官图像融合分析是目前最先进的类器官图像分析方法。本文对类器官图像深度感知技术进行了研究和梳理,介绍了类器官培养机制及其在深度感知中的应用理念,分别综述了类器官图像与分类识别、模式检测、图像分割和动态跟踪等四种深度感知算法的主要进展,并比较分析了不同深度模型的性能优势。此外,本文还从深度感知特征学习、模型泛化和多重评价参数等方面总结了各种器官图像的深度感知技术,并展望了未来基于深度学习方法的器官图像的发展趋势,以期推动深度感知技术在器官图像中的应用。为该领域的学术研究和实际应用提供了重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[A review on depth perception techniques in organoid images].

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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