Self-supervised Pretraining of Cell Segmentation Models

• URL: http://arxiv.org/abs/2604.10609v1
• Date: Sun, 12 Apr 2026 12:29:39 GMT
• Title: Self-supervised Pretraining of Cell Segmentation Models
• Abstract summary: We propose DINOCell, a self-supervised framework for cell instance segmentation.<n>It adapts representations from DINOv2 to microscopy through continued self-supervised training on unlabeled cell images.<n>On the LIVECell benchmark, DINOCell achieves a SEG score of 0.784, improving by 10.42% over leading SAM-based models.
• Score: 37.09823149373976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each cell. However, progress is constrained by the scarcity of high-quality labeled microscopy datasets. Many recent approaches address this challenge by initializing models with segmentation-pretrained weights from large-scale natural-image models such as Segment Anything Model (SAM). However, representations learned from natural images often encode objectness and texture priors that are poorly aligned with microscopy data, leading to degraded performance under domain shift. We propose DINOCell, a self-supervised framework for cell instance segmentation that leverages representations from DINOv2 and adapts them to microscopy through continued self-supervised training on unlabeled cell images prior to supervised fine-tuning. On the LIVECell benchmark, DINOCell achieves a SEG score of 0.784, improving by 10.42% over leading SAM-based models, and demonstrates strong zero-shot performance on three out-of-distribution microscopy datasets. These results highlight the benefits of domain-adapted self-supervised pretraining for robust cell segmentation.

Related papers

• Revisiting foundation models for cell instance segmentation [1.514139641301594]
  We evaluate foundation models for cell segmentation (CellPoseSAM, CellSAM, $$SAM) and for general-purpose segmentation (SAM, SAM2, SAM3) on a diverse set of microscopy datasets.<n>We introduce a new instance segmentation strategy called automatic prompt generation (APG) that can be used to further improve SAM-based microscopy foundation models.<n>Our work provides important lessons for adaptation strategies of SAM-style models to microscopy and provides a strategy for creating even more powerful microscopy foundation models.
  arXiv  Detail & Related papers  (2026-03-18T15:34:42Z)
• Generalization of Self-Supervised Vision Transformers for Protein Localization Across Microscopy Domains [0.254890465057467]
  Self-supervised learning (SSL) can mitigate this by pretraining on large unlabeled datasets.<n>We generate image embeddings using three DINO backbones pretrained on ImageNet-1k, the Human Protein Atlas (HPA), and OpenCell.<n>All pretrained models transfer well, with the microscopy-specific HPA-pretrained model achieving the best performance.
  arXiv  Detail & Related papers  (2026-02-05T10:39:00Z)
• Self-supervised Representation Learning with Local Aggregation for Image-based Profiling [84.52554180480037]
  Image-based cell profiling aims to create informative representations of cell images.<n>Recent developments in non-contrastive Self-Supervised Learning have inspired this paper.<n>We introduce specialized data augmentation and representation post-processing methods tailored to cell images.
  arXiv  Detail & Related papers  (2025-06-17T07:25:57Z)
• Unsupervised cell segmentation by fast Gaussian Processes [6.709057867802485]
  We develop a novel unsupervised cell segmentation algorithm based on fast Gaussian processes for noisy microscopy images.<n>We derived robust thresholding criteria adaptive for heterogeneous images containing distinct brightness at different parts to separate objects from the background.
  arXiv  Detail & Related papers  (2025-05-24T23:28:14Z)
• 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)
• Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen [76.02070962797794]
  This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data.<n>CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy [14.042884268397058]
  This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions.
  arXiv  Detail & Related papers  (2024-04-12T15:45:26Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• 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)
• Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation [0.0]
  Deep learning (DL) shows powerful potential in cell segmentation tasks, but suffers from poor generalization. In this paper, we introduce a novel semi-supervised cell segmentation method called Multi-Microscopic-view Cell semi-supervised (MMCS) MMCS can train cell segmentation models utilizing less labeled multi-posture cell images with different microscopy well. It achieves an F1-score of 0.8239 and the running time for all cases is within the time tolerance.
  arXiv  Detail & Related papers  (2023-03-21T08:08:13Z)
• 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.