CellPilot: A unified approach to automatic and interactive segmentation in histopathology (2411.15514v1)

Abstract: Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathology. However, variability in tissue appearance and cell morphology presents challenges for achieving reliable segmentation, often requiring manual correction to improve accuracy. This work introduces CellPilot, a framework that bridges the gap between automatic and interactive segmentation by providing initial automatic segmentation as well as guided interactive refinement. Our model was trained on over 675,000 masks of nine diverse cell and gland segmentation datasets, spanning 16 organs. CellPilot demonstrates superior performance compared to other interactive tools on three held-out histopathological datasets while enabling automatic segmentation. We make the model and a graphical user interface designed to assist practitioners in creating large-scale annotated datasets available as open-source, fostering the development of more robust and generalized diagnostic models.

• The paper presents CellPilot, a model that merges automatic and interactive segmentation to improve cell and gland delineation in histopathology.
• It leverages a fine-tuned Segment Anything Model (SAM) and CellViT to achieve superior IoU scores compared to competing methods.
• Extensive training on over 675,000 masks from nine datasets underpins its robustness, paving the way for faster, more reliable diagnostics.

An Expert Review of "CellPilot: A Unified Approach to Automatic and Interactive Segmentation in Histopathology"

The paper "CellPilot: A Unified Approach to Automatic and Interactive Segmentation in Histopathology" proposes an innovative framework for cell and gland segmentation in histopathological images. Leveraging the capabilities of deep learning, this paper addresses the complexity and variability inherent in histopathological image segmentation by integrating CellPilot — a model bridging automatic and interactive segmentation — which is a critical task for disease diagnosis, including cancer detection.

Framework Overview

CellPilot is designed to tackle the limitations of existing segmentation approaches, such as the variable performance of automated models and the labor-intensive nature of manual corrections in histopathology. The framework is built on a fine-tuned version of the Segment Anything Model (SAM) and integrates CellViT to deliver both automatic initial segmentation and an interactive refinement process. This dual approach allows users to correct and refine segmentations as needed, thereby enhancing the accuracy and reliability of diagnostics.

Dataset and Training

The model was trained on a substantial dataset comprising over 675,000 masks from nine diverse datasets covering 16 different organs, thus ensuring a comprehensive representation across multiple histopathological contexts. CellPilot's training framework employed low-rank adaptation techniques and interactive prompt simulations, which facilitated robust learning and performance improvement over standard methods.

Evaluation and Comparative Analysis

In evaluating its performance, CellPilot was compared against other interactive models such as MedSAM, SimpleClick, and SAM across three independent datasets. The results demonstrated CellPilot's superior capability in extracting high-quality segmentations for both cells and glands, with performance metrics indicating enhanced mean Intersection over Union (IoU) scores. Notably, the model showed particular strengths in scenarios with initial bounding box prompts, outperforming competitors in both single and iterative prompt settings.

Results and Implications

The numerical results presented underscore the efficacy of the CellPilot model in achieving high initial segmentation accuracy with the flexibility for subsequent refinements through user interaction. Such a system offers significant advantages in clinical settings, allowing for faster and more precise diagnoses while reducing the need for extensive manual corrections. The open-source availability of the model and its graphical user interface fosters further innovation and application across different histopathology tasks, promoting advancements in automated diagnostic tools.

Future Directions

While CellPilot sets a notable benchmark, the paper discusses limitations such as the computational overhead and the challenges in distinguishing between similar histological structures with limited prompts. Addressing these issues would be a worthwhile direction for future research. Moreover, integrating semantic segmentation capabilities could enhance the model's applicability in differentiating between various cell types, enriching its utility in pathological assessments.

In conclusion, CellPilot provides a robust framework for automatic and interactive histological image segmentation, pushing the boundaries of AI-driven diagnostics. This paper presents a substantial step toward developing more adaptive and precise tools for medical image analysis, with its implications extending into practical diagnostic applications and fostering a deeper understanding of histopathological processes.