Cell Segmentation

90 papers with code • 12 benchmarks • 22 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

Libraries

Use these libraries to find Cell Segmentation models and implementations
2 papers
12

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

labmlai/annotated_deep_learning_paper_implementations 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

Deep Learning in Single-Cell Analysis

scverse/scvi-tools 22 Oct 2022

Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.

SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation

xq141839/sppnet 23 Aug 2023

Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

DebeshJha/2020-CBMS-DoubleU-Net 8 Jun 2020

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.

Microscopy Cell Segmentation via Convolutional LSTM Networks

arbellea/LSTM-UNet 29 May 2018

Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task.

Cell Detection with Star-convex Polygons

stardist/stardist 9 Jun 2018

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 7 Mar 2019

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.

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

IVRL/w2s ICLR 2021

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

hvcl/scribble2label 23 Jun 2020

Segmentation is a fundamental process in microscopic cell image analysis.

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

tio-ikim/cellvit 27 Jun 2023

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.