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
Most implemented papers
DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging
In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordings.
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data.
Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features
SAIL-GuoLab/Cell_Segmentation_and_Tracking
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The ability to extrapolate gene expression dynamics in living single cells requires robust cell segmentation, and one of the challenges is the amorphous or irregularly shaped cell boundaries.
Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level.
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
We propose a cell segmentation method for analyzing images of densely clustered cells.
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
t_scherr/cell-segmentation-and-tracking
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In addition, we adapt a graph-based cell tracking algorithm to evaluate our proposed method on the task of cell tracking.
Few-Shot Microscopy Image Cell Segmentation
Yussef93/FewShotCellSegmentation
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Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem.
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.
Accurate and versatile 3D segmentation of plant tissues at cellular resolution
hci-unihd/plant-seg
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eLife 2020
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs.
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
nachifur/LLPC
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The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection.
