Cell Segmentation
78 papers with code • 9 benchmarks • 19 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
uBench
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
labmlai/annotated_deep_learning_paper_implementations
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
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.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
xq141839/sppnet
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Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DebeshJha/2020-CBMS-DoubleU-Net
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The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Guzaiwang/CE-Net
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In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Segmentation is a fundamental process in microscopic cell image analysis.
CellViT: Vision Transformers for Precise Cell Segmentation and Classification
tio-ikim/cellvit
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Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.
Applying Faster R-CNN for Object Detection on Malaria Images
ErickDiaz/bioinformatic_thesis_project
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Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data.
