We identify cells in microscopy images with stained nuclei, using the following process: Candidate seeds for nuclei are identified as extrema in a Laplacian-of-Gaussian space, and weak candidates are eliminated from clusters obtained by ellipse fitting; a region of interest for each nucleus is then defined by combining local and global thresholding; and these regions are repeatedly merged and split by modeling the shape of a nucleus and measuring the roughness of the shared boundaries connected nuclei. This method showed superior abilities to detect the nucleus regions and to split the boundaries of connected nuclei. Our experiments show higher scores in comparison with five other techniques in terms of eight evaluation metrics.

We propose a quantitative analysis method using cell segmentation to evaluate the coating quality of anodic oxide. In order to identify each cell in a titania surface, a boundary region is adaptively detected according to the regional intensity distribution, and a boundary distance map is projected onto the scanning electron microscope (SEM) image of the titania surface. Each cell in the projected image is divided via clustering centered on the local maximum point. The uniformities of the cell distribution, size, and thickness are measured using the angle difference, size difference, and brightness difference between adjacent cells to comprehensively evaluate the quantitative alignment of the titania surface. The proposed method demonstrates the best performance in both cell detection and cell segmentation for SEM images of the titania surface compared with the state-of-the-art methods. The quantitative analysis of the anodic oxide film alignment was verified using color coding, histogram, and quantification of uniformity.

Immunogenic cell death (ICD) can enhance the immunogenicity of cold tumors, convert them into immune-responsive hot tumors, and improve the efficacy of cancer immunotherapy. Because ICD inducers cause cell swelling, membrane rupture, and the release of damage-associated molecular patterns (DAMPs), effective screening of ICD inducers requires a system capable of rapidly and accurately assessing morphological changes and DAMP dynamics on a large scale, thus highlighting the need for advanced image-processing capabilities. In the present study, we developed an artificial intelligence (AI)-based detector to screen for ICD inducers by identifying the typical morphologies of dying cells undergoing ICD. To enhance the performance, we applied transfer learning from fluorescent markers and fine-tuned the model using differential interference contrast (DIC) images. In addition, model-assisted labeling (MAL) improved annotation efficiency by reducing the need for manual labeling in ICD screening. In a blind test, the AI successfully identified three ICD-inducing agents from eight candidates, which were validated through analyses of cell death type, DAMP release, and immune activation. Our AI-based high-throughput screening (HTS) system efficiently identified ICD candidates using only real-time optical images, thereby significantly reducing the time and resources required for screening. In addition, the system demonstrated the ability to detect subtle morphological differences that are difficult to discern through manual analysis, indicating its potential for ICD prediction as well as for foundational research and broader screening applications.