Cell Segmentation Edit Task
Medical • Medical Image Segmentation
About
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
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Greatest papers with code
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on
Cell Segmentation
on DIC-HeLa
CELL SEGMENTATION COLORECTAL GLAND SEGMENTATION: ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
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
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.
Ranked #1 on
Lung Nodule Segmentation
on LUNA
(Accuracy metric)
CELL SEGMENTATION OPTIC DISC SEGMENTATION SEMANTIC SEGMENTATION
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
• yijingru/KG_Instance_Segmentation
• 
In this paper, we propose a new box-based cell instance segmentation method.
CELL SEGMENTATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION
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.
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures
• ChristophReich1996/Cell-DETR
•
For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances.
CELL SEGMENTATION INSTANCE SEGMENTATION SEMANTIC SEGMENTATION
Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features
• SAIL-GuoLab/Cell_Segmentation_and_Tracking
•
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.
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.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
• kuku-sichuan/ComparisonDetector
• 
In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
Faster Mean-shift: GPU-accelerated Embedding-clustering for 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.
CELL SEGMENTATION INSTANCE SEGMENTATION SEMANTIC SEGMENTATION