arXiv subjects

Tomomi Nemoto

Publications and source records attributed to Tomomi Nemoto.

2 recordsLinked to original sources

arXiv2025

Optical turbulence retrieval of heterogeneous media

The transport of intensity equation (TIE) has revolutionized phase retrieval in optical microscopy, yet its application to complex media with absorption/scattering remains challenging. Here, we present a coupled TIE-TPE (transport of phase equation) framework derived directly from the paraxial wave equation with complex optical potential. By decomposing the refractive index field into a spatially uniform mean field and local fluctuation field, our approach enables simultaneous reconstruction of refractive-index fluctuations and attenuation coefficients without linearization assumptions. We establish reconstruction validity bounds that define the measurable parameter region where reconstruction remains physically consistent. Experimental demonstration with microlens arrays and HeLa cells shows robust recovery of optical properties even in the transparent-limit regime where attenuation signals approach detection thresholds. Furthermore, we provide the first experimental verification of attenuation symmetry -- a fundamental property of wave propagation that characterizes reciprocity in light-matter interactions.

physics.optics↗

arXiv2018

A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images

In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors. A fundamental step of the investigation is mitotic cell (cell division) detection. Because of the complex backgrounds (normal cells), the majority of the existing methods cause several false positives. In this paper, we proposed a 2.5D cascaded end-to-end convolutional neural network (CasDetNet) with temporal information to accurately detect automatic mitotic cell in 4D microscopic images with few training data. The CasDetNet consists of two 2.5D networks. The first one is used for detecting candidate cells with only volume information and the second one, containing temporal information, for reducing false positive and adding mitotic cells that were missed in the first step. The experimental results show that our CasDetNet can achieve higher precision and recall compared to other state-of-the-art methods.

cs.CV↗