Standardization, Benchmarking, and Fine-Tuning in Deep Learning-Based Cell Segmentation and Tracking

Abstract

Bioimage analysis workflows for living cells typically involve a sequence of steps, including image acquisition, preprocessing, cell segmentation, object classification, and temporal tracking. Cell segmentation aims to identify and separate individual cells in microscopy images, whereas cell tracking links these segmented objects across time-lapse image sequences to quantify cell movement, morphology, and dynamic behavior. These tasks are challenging because microscopy datasets often vary in image quality and may contain imaging noise, variable staining, overlapping cells, heterogeneous cell morphologies, and changing acquisition conditions. Over the past decade, the field has shifted from heuristic and rule-based image processing toward deep learning-based approaches, supported by advances in convolutional neural networks, foundation models, and increasingly standardized datasets. This transition has been closely connected to the development of interoperable data formats, shared benchmarks, and open-source bioimage analysis ecosystems. The present review discusses this evolution with a focus on standardization, benchmarking, and data-centric model adaptation strategies. In this context, data-centric strategies refer to approaches that improve model performance primarily through better data selection, annotation, fine-tuning, and expert feedback rather than through architecture design alone. Particular attention is given to small-sample fine-tuning and human-in-the-loop workflows, which aim to adapt pretrained segmentation and tracking models to laboratory-specific microscopy data while reducing annotation effort and improving the reliability of downstream biological conclusions.