DeepAI AI Chat
Log In Sign Up

Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks

by   Lei Bi, et al.

Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to be adapt to the clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in resolving segmentation problems. However, the reliance on large annotated training data is problematic when dealing with medical images. If a sufficient amount of annotated training data to cover all possible variations is not available, FCNs do not provide accurate segmentation. In addition, FCNs have a large receptive field in the convolutional layers, and hence produce coarse outputs of boundaries. Hence, we propose a new fully automated method that we refer to as a dual-stage fully convolutional networks (DSFCN). Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details. During training, our approach learns from the training data and the estimated results derived from the previous iteration. The ability to learn from the previous iteration optimizes the learning of the optic cup and the disk boundaries. During testing (prediction), DSFCN uses test (input) images and the estimated probability map derived from previous iterations to gradually improve the segmentation accuracy. Our method achieved an average Dice co-efficient of 0.8488 and 0.9441 for optic cup and disk segmentation and an area under curve (AUC) of 0.9513 for glaucoma detection.


page 2

page 4


Automatic Liver Lesion Detection using Cascaded Deep Residual Networks

Automatic segmentation of liver lesions is a fundamental requirement tow...

A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation

Recent advances in deep learning, like 3D fully convolutional networks (...

A Generalized Motion Pattern and FCN based approach for retinal fluid detection and segmentation

SD-OCT is a non-invasive cross-sectional imaging modality used for diagn...

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

We introduce an accurate lung segmentation model for chest radiographs b...

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

In the field of connectomics, neuroscientists seek to identify cortical ...

Rethinking Fully Convolutional Networks for the Analysis of Photoluminescence Wafer Images

The manufacturing of light-emitting diodes is a complex semiconductor-ma...

Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images

Segmentation maps of medical images annotated by medical experts contain...