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使用DeMemSeg进行重叠前膜的细分和量化详细协议

Shodai Taguchi^1,2,3,4, Keita Chagi⁴, Hiroki Kawai⁴

¹Ph.D. Program in Humanics, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Ibaraki, Japan.

|December 11, 2025

概括

本研究介绍了DeMemSeg,这是一个面具R-CNN模型,用于在2D图像中准确地细分重叠的酵母前膜 (PSM). 这种方法使得用于生物研究的膜形态学的精确定量分析成为可能.

科学领域:

细胞生物学细胞生物学
生物物理学的生物物理.
显微镜的使用方法

背景情况:

对生物膜形态学的定量分析对于理解细胞过程至关重要.
手动注释是艰苦和主观的,而自动化方法在2D显微镜图像中难以重叠结构.

研究的目的:

开发一种可靠和自动化的方法,用于高保真实例细分和对重叠的前膜 (PSM) 的定量分析.
从2D最大强度投影 (MIPs) 来实现单个重叠的膜结构的准确和可重复的细分.

主要方法:

一个步骤逐步的协议,涉及同步刺激诱导,3D光图像采集,并转换为2D MIPs.
使用半自动管道和单细胞隔离的CellPose模型生成定制注释数据集.
培训和应用一个基于R-CNN的面具模型,DeMemSeg,例如细分.

主要成果:

DeMemSeg实现了来自2D MIP的单个重叠PSM的高保真实例细分.
该协议可以提取形态参数 (例如,长度,圆度) 用于定量表型化.
基于膜形态学,精确区分野生类型和突变酵母菌株.

结论:

DeMemSeg框架为分析复杂的膜形态提供了一个客观,高效和可扩展的解决方案.
这种方法可以对动态膜结构,特别是酵母PSM进行详细的定量分析.
与手动注释和生物膜分析的传统自动化方法相比,提供了显著的进步.

关键词:

细胞形态细胞形态深度学习是一种深度学习.实例细分是指实例的细分.面具 R-CNN 的意思显微镜是图像处理的技术.重叠的对象是重叠的对象.定量图像分析是一种定量图像分析.酵母菌的分泌体是酵母的分泌体.

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