Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
CELL SEGMENTATION DATA AUGMENTATION ELECTRON MICROSCOPY IMAGE SEGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
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 Lung Nodule Segmentation on LUNA
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).
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
#2 best model for Retinal Vessel Segmentation on STARE (F1 score metric)
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.
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.