CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
This work addresses the need for energy-efficient and adaptive methods in digital pathology to reduce reliance on extensive annotated datasets and predefined classification schemes, with incremental improvements in efficiency and adaptability.
The paper tackles the problem of cell segmentation and classification in digital pathology by proposing CellViT++, a framework that uses Vision Transformers with foundation models to achieve zero-shot segmentation and data-efficient classification, demonstrating excellent performance on seven diverse datasets and surpassing manually labeled data with automated dataset generation.
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.