Cell Segmentation
65 papers with code • 9 benchmarks • 18 datasets
Cell Segmentation is a task of splitting a microscopic image domain into segments, which represent individual instances of cells. It is a fundamental step in many biomedical studies, and it is regarded as a cornerstone of image-based cellular research. Cellular morphology is an indicator of a physiological state of the cell, and a well-segmented image can capture biologically relevant morphological information.
Source: Cell Segmentation by Combining Marker-controlled Watershed and Deep Learning
Benchmarks
These leaderboards are used to track progress in Cell Segmentation
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Datasets
STARE
DigestPath
LIVECell
Fluo-N2DL-HeLa
Latest papers
Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's built-in algorithm, StarDist and Cellpose.
Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images
dair-iitd/guided-prompting-sam
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In response, we investigate guiding the prompting procedure in SAM for weakly supervised cell segmentation when only bounding box supervision is available.
Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
lhaof/cellseg
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Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
xq141839/sppnet
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Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
cpystan/psm
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The gradient information in the shallow layers of the network is aggregated to generate prior self-activation maps.
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation
In this paper, we propose the multi-label CircleSnake model for instance segmentation on Eos.
Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps
In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach.
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
tio-ikim/cellvit
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Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.
Scribble-supervised Cell Segmentation Using Multiscale Contrastive Regularization
The results show that the proposed multiscale contrastive loss is effective in improving the performance of S2L, which is comparable to that of the supervised learning segmentation method.
