Deep Learning-based Image Cytometry Using a Bit-pattern Kernel-filtering Algorithm to Avoid Multi-counted Cell Determination

Abstract

Background/Aim: In pathology, the digitization of tissue slide images and the development of image analysis by deep learning have dramatically increased the amount of information obtainable from tissue slides. This advancement is anticipated to not only aid in pathological diagnosis, but also to enhance patient management. Deep learning-based image cytometry (DL-IC) is a technique that plays a pivotal role in this process, enabling cell identification and counting with precision. Accurate cell determination is essential when using this technique. Herein, we aimed to evaluate the performance of our DL-IC in cell identification. Materials and Methods: Cu-Cyto, a DL-IC with a bit-pattern kernel-filtering algorithm designed to help avoid multi-counted cell determination, was developed and evaluated for performance using tumor tissue slide images with immunohistochemical staining (IHC). Results: The performances of three versions of Cu-Cyto were evaluated according to their learning stages. In the early stage of learning, the F1 score for immunostained CD8⁺ T cells (0.343) was higher than the scores for non-immunostained cells [adenocarcinoma cells (0.040) and lymphocytes (0.002)]. As training and validation progressed, the F1 scores for all cells improved. In the latest stage of learning, the F1 scores for adenocarcinoma cells, lymphocytes, and CD8⁺ T cells were 0.589, 0.889, and 0.911, respectively. Conclusion: Cu-Cyto demonstrated good performance in cell determination. IHC can boost learning efficiencies in the early stages of learning. Its performance is expected to improve even further with continuous learning, and the DL-IC can contribute to the implementation of precision oncology.