Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Abstract: Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78\% and a Dice coefficient of 94.73\% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.

22/11/2021

BMM-Net: automatic segmentation of edema in optical coherence tomography based on boundary detection and multi-scale network

Ruru Zhang, Jiawen He, Shenda Shi and
Haihong E, Zhonghong Ou, Meina Song

Keywords Paper

Applied computing, Life and medical sciences, Computational biology, Imaging, Computing methodologies, Artificial intelligence, Computer vision, Computer vision problems, Image segmentation, Computer vision tasks, Biometrics

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Latent optimization, StyleGAN, Image Editing, Chest X-ray, Image manipulation, constrained optimization, Disease progression, Disease quantification, Manifold, Latent space traversal

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05/01/2021

Jing Wang, Yiwei Chen, Wanyue Li and
Wen Kong, Yi He, Chuihui Jiang, Guohua Shi

inverse problem, variational method, deep learning, convolutional neural network, optimal control problem, gradient flow, image denoising, single image super-resolution, magnetic resonance imaging, computed tomography

4:56

06/07/2020

David Tellez, Diederik Höppener, Cornelis Verhoef and
Dirk Grünhagen, Pieter Nierop, Michal Drozdzal, Jeroen Laak, Francesco Ciompi

Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Relative Positional Map