Cell Segmentation
65 papers with code • 9 benchmarks • 18 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
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Datasets
STARE
DigestPath
LIVECell
Fluo-N2DL-HeLa
Latest papers with no code
Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations
Overall, this research aims to guide researchers in effectively utilizing cell segmentation models in the presence of minor optical aberrations.
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
In this paper, we report a new large red blood cell (RBC) image dataset and propose a two-stage deep learning framework for RBC image segmentation and classification.
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields
It is composed of a denoising diffusion probabilistic model (DDPM) generating high-fidelity synthetic cell microscopy images and a flow prediction model (FPM) predicting the non-rigid transformation between consecutive video frames.
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking.
Pushing the limits of cell segmentation models for imaging mass cytometry
Our results show that removing 50% of cell annotations from GT masks only reduces the dice similarity coefficient (DSC) score to 0. 874 (from 0. 889 achieved by a model trained on fully annotated GT masks).
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells
Moreover, a novel frequency domain denoising network, named FDNet, is proposed for astrocyte segmentation.
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational complexity.
Morphological Profiling for Drug Discovery in the Era of Deep Learning
Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations
In recent years, several unsupervised cell segmentation methods have been presented, trying to omit the requirement of laborious pixel-level annotations for the training of a cell segmentation model.
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain
This is the basis to study the multi-scale architecture of the brain regarding its subdivision into brain areas and nuclei, cortical layers, columns, and cell clusters down to single cell morphology Methods for brain mapping and cell segmentation exploit such images to enable rapid and automated analysis of cytoarchitecture and cell distribution in complete series of histological sections.
