Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks.

We present an optochemical O2 scavenging system that enables precise spatiotemporal control of the level of hypoxia in living cells simply by adjusting the light intensity in the illuminated region. The system employs rhodamine containing a selenium or tellurium atom as an optochemical oxygen scavenger that rapidly consumes O2 by photochemical reaction with glutathione as a coreductant upon visible light irradiation (560–590 nm) and has a rapid response time, within a few minutes. The glutathione‐consuming quantum yields of the system were calculated as about 5 %. The spatiotemporal O2 consuming in cultured cells was visualized with a hypoxia‐responsive fluorescence probe, MAR. Phosphorescence lifetime imaging was applied to confirmed that different light intensities could generate different levels of hypoxia. To illustrate the potential utility of this system for hypoxia research, we show that it can spatiotemporally control calcium ion (Ca2+) influx into HEK293T cells expressing the hypoxia‐responsive Ca2+ channel TRPA1.

Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. However, to study cell morphology and connectivity in organs such as brains, scientists must first perform cell segmentation, which involves extracting individual cell regions of various shapes and sizes from a 3D image. This remains a great challenge because automatic cell segmentation can contain numerous errors, even with advanced deep learning methods. For biomedical research that requires cell segmentation in large 3D image stacks, an efficient semi-automated software solution is still needed. We created Seg2Link, which generates automatic segmentations based on deep learning predictions and allows users to quickly correct errors in the segmentation results. It can perform automatic instance segmentation of 2D cells in each slice, 3D cell linking across slices, and various manual corrections, in order to efficiently transform inaccurate deep learning predictions into accurate segmentation results. Seg2Link's data structure and algorithms were also optimized to process 3D images with billions of voxels on a personal computer quickly. Thus, Seg2Link offers a simple and effective way for scientists to study cell morphology and connectivity in 3D image stacks.