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
Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation
In this paper, we introduce a novel semi-supervised cell segmentation method called Multi-Microscopic-view Cell semi-supervised Segmentation (MMCS), which can train cell segmentation models utilizing less labeled multi-posture cell images with different microscopy well.
SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain
Although the landscape of experimental methods has changed dramatically since the beginning of SpaceTx, the need for quantitative and detailed benchmarking of spatial transcriptomics methods in the brain is still unmet.
Double U-Net for Super-Resolution and Segmentation of Live Cell Images
Accurate segmentation of live cell images has broad applications in clinical and research contexts.
Learning Melanocytic Cell Masks from Adjacent Stained Tissue
Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths.
Scale Equivariant U-Net
Therefore, this paper introduces the Scale Equivariant U-Net (SEU-Net), a U-Net that is made approximately equivariant to a semigroup of scales and translations through careful application of subsampling and upsampling layers and the use of aforementioned scale-equivariant layers.
Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems
To explore heterogeneity of CatIB development during the cultivation and track the size and quantity of CatIBs over time, a hybrid image processing pipeline approach was developed, which combines an ML-based detection of in-focus cells with model-based segmentation.
Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation
Current cell segmentation methods systematically apply stain normalization as a preprocessing step, but the impact brought by color variation has not been quantitatively investigated yet.
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process.
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation
This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation.
A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
Still, the proposed method performs on par with ML-based segmentation approaches usually used in this context.
