Cell Segmentation

38 papers with code • 8 benchmarks • 10 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

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.

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.

Applying Faster R-CNN for Object Detection on Malaria Images

ErickDiaz/bioinformatic_thesis_project 25 Apr 2018

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.

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

HzFu/MNet_DeepCDR 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.

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.

Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells

nachifur/automatic-label-correction-CCEDD 5 Oct 2020

The experiments on the dataset for training show that our automatic label correction algorithm can improve the accuracy of manual labels and further improve the positioning accuracy of overlapping cells with deep learning models.

Microscopy Cell Segmentation via Adversarial Neural Networks

arbellea/DeepCellSeg 18 Sep 2017

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

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.

Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario

kuku-sichuan/ComparisonDetector 14 Oct 2018

In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.