A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images

TL;DR

This paper introduces a lightweight, extensible cell segmentation and classification model for whole slide images that improves accuracy, reduces computational demands, and integrates into digital pathology workflows.

Contribution

The study presents a novel approach combining data refinement, foundation model utilization, knowledge distillation, and workflow integration for enhanced cell analysis in digital pathology.

Findings

Improved segmentation and classification performance with H-Optimus model.

Model size reduced by a factor of 48 with maintained performance.

Enhanced alignment with cell counts and segmentation quality.

Abstract

Developing clinically useful cell-level analysis tools in digital pathology remains challenging due to limitations in dataset granularity, inconsistent annotations, high computational demands, and difficulties integrating new technologies into workflows. To address these issues, we propose a solution that enhances data quality, model performance, and usability by creating a lightweight, extensible cell segmentation and classification model. First, we update data labels through cross-relabeling to refine annotations of PanNuke and MoNuSAC, producing a unified dataset with seven distinct cell types. Second, we leverage the H-Optimus foundation model as a fixed encoder to improve feature representation for simultaneous segmentation and classification tasks. Third, to address foundation models' computational demands, we distill knowledge to reduce model size and complexity while maintaining comparable performance. Finally, we integrate the distilled model into QuPath, a widely used open-source digital pathology platform. Results demonstrate improved segmentation and classification performance using the H-Optimus-based model compared to a CNN-based model. Specifically, average $R^2$ improved from 0.575 to 0.871, and average $PQ$ score improved from 0.450 to 0.492, indicating better alignment with actual cell counts and enhanced segmentation quality. The distilled model maintains comparable performance while reducing parameter count by a factor of 48. By reducing computational complexity and integrating into workflows, this approach may significantly impact diagnostics, reduce pathologist workload, and improve outcomes. Although the method shows promise, extensive validation is necessary prior to clinical deployment.