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
A Foundation Model for Cell Segmentation
Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive.
Defining the boundaries: challenges and advances in identifying cells in microscopy images
Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images.
DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking
Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells.
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model.
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging.
Semi-supervised Instance Segmentation with a Learned Shape Prior
To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Impact of Image Compression on In Vitro Cell Migration Analysis
We aim to identify the most suitable compression algorithm that can be used for this purpose, relating rate–distortion performance as measured in terms of peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index (MS-SSIM) to the segmentation accuracy obtained by the segmentation algorithms.
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.
