Cell Segmentation
59 papers with code • 8 benchmarks • 16 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
DigestPath
LIVECell
PhC-C2DH-U373
Most implemented papers
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Guzaiwang/CE-Net
•
•
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DebeshJha/2020-CBMS-DoubleU-Net
•
•
The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Segmentation is a fundamental process in microscopic cell image analysis.
Applying Faster R-CNN for Object Detection on Malaria Images
ErickDiaz/bioinformatic_thesis_project
•
•
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data.
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
looooongChen/instance_segmentation_with_pixel_embeddings
•
•
The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd.
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.
