Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation

• URL: http://arxiv.org/abs/2603.14321v1
• Date: Sun, 15 Mar 2026 10:39:47 GMT
• Title: Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation
• Abstract summary: We introduce the Personalized Cell task, which aims to segment all cells of a specific type given a reference cell.<n>As a pioneering solution, we propose PerCS-DINO, a framework built on the DINOv2 backbone.
• Score: 4.153850400225319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• CellVerse: Do Large Language Models Really Understand Cell Biology? [74.34984441715517]
  We introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data.<n>We systematically evaluate the performance across 14 open-source and closed-source LLMs ranging from 160M to 671B on CellVerse.
  arXiv  Detail & Related papers  (2025-05-09T06:47:23Z)
• CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models [1.7674154313605157]
  $textCellViTscriptstyle ++$ is a framework for generalized cell segmentation in digital pathology.<n>$textCellViTscriptstyle ++$ is an open-source framework featuring a user-friendly, web-based interface for visualization and annotation.
  arXiv  Detail & Related papers  (2025-01-09T14:26:50Z)
• CellSeg1: Robust Cell Segmentation with One Training Image [37.60000299559688]
  We introduce CellSeg1, a solution for segmenting cells of arbitrary morphology and modality with a few dozen cell annotations in 1 image.<n>Tested on 19 diverse cell datasets, CellSeg1 trained on 1 image achieved 0.81 average mAP at 0.5 IoU, performing comparably to existing models trained on over 500 images.
  arXiv  Detail & Related papers  (2024-12-02T11:55:22Z)
• Cell as Point: One-Stage Framework for Efficient Cell Tracking [56.98130311648714]
  We propose CAP, a novel end-to-end one-stage framework that reimagines cell tracking by treating Cell as Point.<n>Unlike traditional methods, CAP eliminates the need for explicit detection or segmentation, instead jointly tracking cells for sequences in one stage by leveraging the inherent correlations among their trajectories.<n>Cap demonstrates promising cell tracking performance and is 8 to 32 times more efficient than existing methods.
  arXiv  Detail & Related papers  (2024-11-22T10:16:35Z)
• LangCell: Language-Cell Pre-training for Cell Identity Understanding [3.6518971609937068]
  We introduce LangCell, a unified representation of single-cell data and natural language during the pre-training phase. Results show that LangCell is the only single-cell PLM that can work effectively in zero-shot cell identity understanding scenarios.
  arXiv  Detail & Related papers  (2024-05-09T10:04:05Z)
• Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation [50.407071700154674]
  We propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL) Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible. Extensive experiments on a couple of publicly accessible datasets demonstrate that SGFSIS can outperform other annotation-efficient learning baselines.
  arXiv  Detail & Related papers  (2024-02-26T03:49:18Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations [9.335273591976648]
  We present CellMixer, an innovative annotation-free approach for the semantic segmentation of heterogeneous cell populations. Our results show that CellMixer can achieve competitive segmentation performance across multiple cell types and imaging modalities.
  arXiv  Detail & Related papers  (2023-12-01T15:50:20Z)
• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images [66.79688768141814]
  We develop an automatic cell classification pipeline to label microscopy images. We then train a classification model based on the category labels. We deploy two types of segmentation models to segment cells with roundish and irregular shapes.
  arXiv  Detail & Related papers  (2023-10-22T08:11:08Z)
• OCELOT: Overlapped Cell on Tissue Dataset for Histopathology [13.691924123273004]
  We release OCELOT, a dataset dedicated to the study of cell-tissue relationships for cell detection in histopathology. We propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score.
  arXiv  Detail & Related papers  (2023-03-23T08:57:11Z)
• Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation [71.50526869670716]
  We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
  arXiv  Detail & Related papers  (2020-07-21T14:05:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.