Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
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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
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.
In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.
Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images.
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
Ranked #2 on Lung Nodule Segmentation on LUNA
Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions and octave transposed convolutions for learning multiple-spatial-frequency features, thus can better capture retinal vasculatures with varying sizes and shapes.
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).
Ranked #5 on Retinal Vessel Segmentation on CHASE_DB1
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension.
Ranked #3 on Retinal Vessel Segmentation on DRIVE
In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation.