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

Most implemented papers

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.

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.

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.

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.

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.

Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

looooongChen/instance_segmentation_with_pixel_embeddings 21 Apr 2020

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 7 Dec 2022

Cell segmentation is a fundamental task for computational biology analysis.