Professor Kohsuke Takeda and his research team from the Department of Cell Regulation, Graduate School of Biomedical Sciences, Nagasaki University, have developed a proprietary image analysis technology called OrgaMeas, which enables high-precision and efficient analysis of organelle conditions from live-cell imaging data using artificial intelligence (AI) technology. In this study, Taiki Baba, a third-year doctoral student and DC1 Research Fellow of the Japan Society for the Promotion of Science, who belongs to the same department, recognized the need for a new image analysis method during his research. He took the lead in developing this technology while acquiring advanced AI techniques.

Research Abstract

Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and laborious. Here, we present a highly accurate high-throughput image analysis pipeline called OrgaMeas for measuring the morphology and dynamics of organelles. This pipeline mainly consists of two deep learning-based tools: OrgaSegNet and DIC2Cells. OrgaSegNet quantifies many aspects of different organelles by precisely segmenting them. To further process the segmented data at a single-cell level, DIC2Cells automates ROI settings through accurate segmentation of individual cells in differential interference contrast (DIC) images. This pipeline was designed to be low cost and require less coding, to provide an easy-to-use platform. Thus, we believe that OrgaMeas has potential to be readily applied to basic biomedical research, and hopefully to other practical uses such as drug discovery.

KEY WORDS: organelle, image analysis, artificial intelligence, deep learning

Journal Information

Journal: Biochimica et Biophysica Acta (BBA) – Molecular Cell Research

Title: OrgaMeas: A pipeline that integrates all the processes of organelle image analysis

Authors: Taiki Baba*, Akimi Inoue, Susumu Tanimura, and Kohsuke Takeda*

Department of Cell Regulation, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852-8521, Japan

*Corresponding Authors:

Taiki Baba (ORCID: 0000-0003-0924-0134)

Kohsuke Takeda (ORCID: 0000-0002-8359-8399)

DOI: 10.1016/j.bbamcr.2025.119964

Publication Date: April 24, 2025

For details on this research, please refer to the journal below.

URL: https://www.sciencedirect.com/science/article/pii/S0167488925000692