Spherical object segmentation in digital holographic microscopy by deep-learning
Spherical object segmentation in digital holographic microscopy by deep-learning
复制标题
通过深度学习进行数字全息显微镜中的球形物体分割
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Fournier
中科院分区：
文献类型：
--
作者：
Carlos Valadares;D. Brault;L. Denis;C. Fournier
数字全息显微术可以对吸收和半透明物体成像。由于双像和离焦物体的存在，从后向传播全息图中分割物体的任务是具有挑战性的。本文研究了使用深度神经网络来联合收割机反向传播波的真实的部和虚部并产生分割。的网络，训练对反向传播的模拟全息图和地面实况分割，表现良好，即使在训练步骤中使用的全息图的散焦距离和测试时间的全息图的实际散焦距离之间的不匹配的情况下。
Digital holographic microscopy can image both absorbing and translucent objects. Due to the presence of twin-images and out-of-focus objects, the task of segmenting the objects from a back-propagated hologram is challenging. This paper investigates the use of deep neural networks to combine the real and imaginary parts of the back-propagated wave and produce a segmentation. The network, trained with pairs of back-propagated simulated holograms and ground truth segmentations, is shown to perform well even in the case of a mismatch between the defocus distance of the holograms used during the training step and the actual defocus distance of the holograms at test time.