Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
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Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
SOTA for Retinal Vessel Segmentation on DRIVE
The morphological attributes of retinal vessels, such as length, width, tortuosity and branching pattern and angles, play an important role in diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis.
In this paper, we propose an effective and efficient method for vessel segmentation in color fundus images using encoder-decoder based octave convolution network.
To address this limitation, we propose a novel, stochastic training scheme for deep neural networks that better classifies the faint, ambiguous regions of the image.
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them.
A LadderNet has more paths for information flow because of skip connections and residual blocks, and can be viewed as an ensemble of Fully Convolutional Networks (FCN).
#2 best model for Retinal Vessel Segmentation on CHASE_DB1
We propose a novel deep-learning-based system for vessel segmentation.
SOTA for Retinal Vessel Segmentation on HRF
Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods.
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Retinal Vessel Segmentation on STARE
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
#2 best model for Skin Cancer Segmentation on Kaggle Skin Lesion Segmentation