Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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 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.
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 #3 on Retinal Vessel Segmentation on CHASE_DB1
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
In this work, we propose the Residual Spatial Attention Network (RSAN) for retinal vessel segmentation.
We propose a novel deep-learning-based system for vessel segmentation.
Ranked #1 on Retinal Vessel Segmentation on HRF