Cell Segmentation

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

Libraries

Datasets

Subtasks

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

Content

Deep Learning in Single-cell Analysis

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

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

Cell Detection with Star-convex Polygons

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

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

Applying Faster R-CNN for Object Detection on Malaria Images

Microscopy Cell Segmentation Via Convolutional LSTM Networks