IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks (2019-12-12T00:00:00.000000Z)

TL;DR

This work proposes IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image, to improve the performance of vessel segmentation.

Abstract

Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.

Authors

Yuta Nakashima

8 papers

Liangzhi Li

2 papers

Manisha Verma

2 papers

TL;DR

Abstract

Authors

References29 items

Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging

NAS-Unet: Neural Architecture Search for Medical Image Segmentation

Probability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation

DUNet: A deformable network for retinal vessel segmentation

Weighted Res-UNet for High-Quality Retina Vessel Segmentation

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Fully Convolutional Adaptation Networks for Semantic Segmentation

Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Temporal changes in retinal vascular parameters associated with successful panretinal photocoagulation in proliferative diabetic retinopathy: A prospective clinical interventional study

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model

Iterative deep convolutional encoder-decoder network for medical image segmentation

Deformable Convolutional Networks

Learning normalized inputs for iterative estimation in medical image segmentation

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Densely Connected Convolutional Networks

A new tool to connect blood vessels in fundus retinal images

U-Net: Convolutional Networks for Biomedical Image Segmentation

Iterative Vessel Segmentation of Fundus Images

Fully convolutional networks for semantic segmentation

The Value of Fundoscopy in General Practice

A Function for Quality Evaluation of Retinal Vessel Segmentations

Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program.

Ridge-based vessel segmentation in color images of the retina

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

Computer Methods and Programs in Biomedicine

brings much bigger time cost

Field of Study

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