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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
STARE
DigestPath
LIVECell
Fluo-N2DL-HeLa
Most implemented papers
Contour Proposal Networks for Biomedical Instance Segmentation
FZJ-INM1-BDA/celldetection
•
•
We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities.
Training a deep learning model for single-cell segmentation without manual annotation
jiyuuchc/cellcutter
•
•
Scientific Reports, Article number: 23995 (2021) 2021
Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
lee-gihun/mediar
•
•
Cell segmentation is a fundamental task for computational biology analysis.
Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
jiyuuchc/lacss
•
•
This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model.
Microscopy Cell Segmentation via Adversarial Neural Networks
We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach.
Microscopy Cell Segmentation via Convolutional LSTM Networks
Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
kuku-sichuan/ComparisonDetector
•
•
In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement
We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes.
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
yijingru/KG_Instance_Segmentation
•
•
In this paper, we propose a new box-based cell instance segmentation method.
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
2023-MindSpore-1/ms-code-214
•
•
In this paper, we propose a novel Instance Relation Network (IRNet) for robust overlapping cell segmentation by exploring instance relation interaction.
