Automated Cellular Segmentation in Histopathology: A Multi-Step Mask Prediction Approach for Enhanced Analysis

Accurate cellular segmentation is crucial in histopathological analysis for diagnosing diseases and quantifying morphological features. Traditional manual segmentation methods are time-intensive, prone to error, and lack scalability. This study proposes a modified version of the CellPose algorithm, designed specifically for cellular segmentation in histopathological images, to address these challenges. The approach employs a multi-step pipeline encompassing advanced preprocessing, boundary refinement, and post-processing techniques to enhance segmentation accuracy. The modified algorithm was evaluated against baseline models, including R-Mask Convolutional Neural network, U-Net, and the original CellPose, using metrics such as Dice coefficient, Intersection-Over-Union (IoU), precision, recall, and F1 score. Results show that the Modified CellPose achieved superior performance, with a Dice coefficient of 0.91 and the lowest hybrid loss across training epochs, demonstrating robust and precise segmentation capabilities. The method effectively addressed challenges like overlapping cells, varying morphologies, and imaging artifacts, enabling accurate isolation of cellular components.This work underscores the importance of tailoring deep learning models to the unique complexities of histopathological data. By providing pathologists with a powerful and efficient segmentation tool, this study contributes to advancing automated diagnostic workflows, improving objectivity, and enhancing disease characterization.