Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation
This work addresses the limitation of generic cell segmentation methods for biologists and medical researchers by enabling differentiation of specific cell types, though it is incremental as it builds on existing deep learning models.
The paper tackles the problem of segmenting specific cell types in biological and medical imaging by introducing the Personalized Cell Segmentation (PerCS) task, which uses a reference cell to guide segmentation, and proposes PerCS-DINO, a framework that achieves effective segmentation as demonstrated through experiments on a benchmark of 1,372 images and over 110,000 annotated cells.
Accurate cell segmentation is critical for biological and medical imaging studies. Although recent deep learning models have advanced this task, most methods are limited to generic cell segmentation, lacking the ability to differentiate specific cell types. In this work, we introduce the Personalized Cell Segmentation (PerCS) task, which aims to segment all cells of a specific type given a reference cell. To support this task, we establish a benchmark by reorganizing publicly available datasets, yielding 1,372 images and over 110,000 annotated cells. As a pioneering solution, we propose PerCS-DINO, a framework built on the DINOv2 backbone. By integrating image features and reference embeddings via a cross-attention transformer and contrastive learning, PerCS-DINO effectively segments cells matching the reference. Extensive experiments demonstrate the effectiveness of the proposed PerCS-DINO and highlight the challenges of this new task. We expect PerCS to serve as a useful testbed for advancing research in cell-based applications.