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
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.
Towards Early Prediction of Human iPSC Reprogramming Success
abhineet123/ipsc_prediction
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This paper presents advancements in automated early-stage prediction of the success of reprogramming human induced pluripotent stem cells (iPSCs) as a potential source for regenerative cell therapies. The minuscule success rate of iPSC-reprogramming of around $ 0. 01% $ to $ 0. 1% $ makes it labor-intensive, time-consuming, and exorbitantly expensive to generate a stable iPSC line.
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases.
Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
jiyuuchc/lacss
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This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model.
An Instance Segmentation Dataset of Yeast Cells in Microstructures
christophreich1996/yeast-in-microstructures-dataset
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The aim of the dataset and evaluation strategy is to facilitate the development of new cell segmentation approaches.
Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation
Deep learning provides us with powerful methods to perform nucleus or cell segmentation with unprecedented quality.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
lee-gihun/mediar
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Cell segmentation is a fundamental task for computational biology analysis.
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
FZJ-INM1-BDA/celldetection
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NeurIPS CellSeg 2022 2022
In the context of the NeurIPS 22 Cell Segmentation Challenge, the proposed solution is shown to generalize well in a multi-modality setting, while respecting domain-specific requirements such as focusing on specific cell types.
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
