CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models (2501.05269v1)

Abstract: Digital Pathology is a cornerstone in the diagnosis and treatment of diseases. A key task in this field is the identification and segmentation of cells in hematoxylin and eosin-stained images. Existing methods for cell segmentation often require extensive annotated datasets for training and are limited to a predefined cell classification scheme. To overcome these limitations, we propose $\text{CellViT}^{{\scriptscriptstyle ++}}$, a framework for generalized cell segmentation in digital pathology. $\text{CellViT}^{{\scriptscriptstyle ++}}$ utilizes Vision Transformers with foundation models as encoders to compute deep cell features and segmentation masks simultaneously. To adapt to unseen cell types, we rely on a computationally efficient approach. It requires minimal data for training and leads to a drastically reduced carbon footprint. We demonstrate excellent performance on seven different datasets, covering a broad spectrum of cell types, organs, and clinical settings. The framework achieves remarkable zero-shot segmentation and data-efficient cell-type classification. Furthermore, we show that $\text{CellViT}^{{\scriptscriptstyle ++}}$ can leverage immunofluorescence stainings to generate training datasets without the need for pathologist annotations. The automated dataset generation approach surpasses the performance of networks trained on manually labeled data, demonstrating its effectiveness in creating high-quality training datasets without expert annotations. To advance digital pathology, $\text{CellViT}^{{\scriptscriptstyle ++}}$ is available as an open-source framework featuring a user-friendly, web-based interface for visualization and annotation. The code is available under https://github.com/TIO-IKIM/CellViT-plus-plus.

• The paper introduces an energy-efficient framework that integrates Vision Transformers with foundation models for adaptive cell segmentation and classification.
• It employs a retrainable lightweight classification module alongside a fixed segmentation encoder to update new cell types without full model retraining.
• Evaluated on multiple datasets, including PanNuke and Ocelot, the method delivers strong zero-shot performance with a reduced computational footprint.

Overview of "CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models"

This paper presents CellViT++, an innovative framework designed to enhance cell segmentation and classification in digital pathology through the use of Vision Transformers and foundation models. The focus is on creating a generalized and flexible system capable of adapting to various cell types with reduced data requirements and energy consumption. CellViT++ builds on the foundation of its predecessor, CellViT, by incorporating a more comprehensive approach that leverages foundation models such as HIPT, UNI, Virchow, and SAM-H for improved performance across diverse histopathological tasks.

Methodology and Features

CellViT++ utilizes Vision Transformers (ViTs) with foundation models as encoders to simultaneously compute deep cell features and generate segmentation masks. A key feature of the framework is its adaptability to new cell types, achieved by retraining a lightweight classification module without modifying the segmentation model, thus maintaining computational efficiency. This is particularly useful in clinical scenarios where new cell types frequently emerge, requiring consistent updates to the classification schemes employed in digital pathology.

The framework is trained on the PanNuke dataset, consisting of 19 organ types and approximately 190,000 annotated cells, ensuring robust performance across various cell classes. It excels in zero-shot segmentation, where it significantly outperforms existing methods without requiring extensive new data for training. This capability is bolstered by the use of immunofluorescence-stained images to generate training datasets, eliminating the need for pathologist annotations and thus reducing the time and resources typically required for such tasks.

Performance and Results

The paper highlights CellViT++'s excellent performance on seven different datasets, showcasing its versatility and generalizability in handling a wide array of cell types and clinical settings. Notably, it achieves remarkable results in data-efficient cell-type classification, outperforming traditional methods in both speed and accuracy. With minimal data, CellViT++ maintains high performance while drastically reducing the computational carbon footprint compared to conventional convolutional neural network-based approaches.

Moreover, the framework's data-efficient nature is exemplified in its performance on the Ocelot dataset, where a subset of the training data yields competitive results against fully trained models. Such efficiency is critical in practical implementations, where obtaining large annotated datasets is often a significant hurdle.

Implications and Future Prospects

The development of CellViT++ represents a significant advancement in the field of digital pathology by providing an adaptable and efficient tool for cell analysis. Its open-source availability and user-friendly interface make it accessible for broader adoption in both research and clinical settings. The paper suggests that such tools can significantly expedite biomarker discovery and improve diagnostic accuracy, ultimately enhancing patient care.

Looking forward, the approach outlined in CellViT++ could lead to seamless integration of AI into routine pathology workflows. Future research may focus on further optimizing the framework for real-time applications and expanding its capabilities to cover a broader range of pathological conditions. Additionally, addressing the computational requirements for training foundation models remains a critical area that needs attention to make such advancements sustainable.

In conclusion, CellViT++ offers a compelling solution for cell segmentation and classification, setting a new standard in digital pathology with its energy-efficient and adaptive design. Its deployment can potentially transform clinical diagnostics and research methodologies, paving the way for a new era of AI-driven pathology.