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在使用物理驱动的神经网络的数字全息显微镜中对生物细胞的三维形态进行一次性重建

Jihwan Kim¹, Youngdo Kim¹, Hyo Seung Lee¹

¹Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.

Nature communications

|May 24, 2025

概括

本研究介绍了MorpHoloNet,这是一个深度学习模型,用于从单个全息图像中使用数字直线全息显微镜 (DIHM) 来重建3D细胞形态. 这种方法克服了生物细胞分析现有技术的局限性.

科学领域:

生物物理学的生物物理.
计算机成像成像技术
深度学习用于显微镜.

背景情况:

数字直线全息显微镜 (DIHM) 具有先进的相位检索.
目前的方法难以从生物细胞的单次全息图中进行3D形态重建.

研究的目的:

从DIHM开发一个深度学习模型,用于从DIHM进行单次3D形态重建.
从有限的全息数据中重建生物样本的复杂3D结构的局限性.

主要方法:

提出了MorpHoloNet,集成物理驱动和基于坐标的神经网络.
模拟光学衍射并通过最小化全息图重建损失来优化模型.
从单次全息图中启用了直接的3D复杂光场和形态重建.

主要成果:

从单次全息图中成功重建了3D形态,没有相位移动或角度扫描.
具备直接3D复杂光场重建的能力.
MorpHoloNet有效地绕过了对多个全息图或扫描的需求.

结论:

在DIHM中,MorpHoloNet提供了一种新的解决方案,用于在DIHM中进行一次性3D形态重建.
该模型有助于研究生物细胞中的动态3D行为和形态变化.
这一进步对活细胞成像和分析有重大影响.

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