Cell Segmentation
38 papers with code • 8 benchmarks • 10 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
EVICAN
LIVECell
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.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
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.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
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.
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.
Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells
nachifur/automatic-label-correction-CCEDD
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The experiments on the dataset for training show that our automatic label correction algorithm can improve the accuracy of manual labels and further improve the positioning accuracy of overlapping cells with deep learning models.
Microscopy Cell Segmentation via Adversarial Neural Networks
We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach.
Microscopy Cell Segmentation via Convolutional LSTM Networks
Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
kuku-sichuan/ComparisonDetector
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In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
