DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook

Chudzik, Piotr and Al-Diri, Bashir and Caliva, Francesco and Hunter, Andrew (2018) DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . pp. 5934-5937. ISSN 1557-170X

Full content URL: https://doi.org/10.1109/EMBC.2018.8513604

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DISCERN: Generative Framework for Vessel Segmentation using Convolutional Neural Network and Visual Codebook
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Abstract

This paper presents a novel two-stage vessel segmentation framework applied to retinal fundus images. In the first stage a convolutional neural network (CNN) is used to correlate an image patch with a corresponding groundtruth reduced using Totally Random Trees Embedding. In the second stage training patches are forward propagated through CNN to create a visual codebook. The codebook is used to build a generative nearest neighbour search space that can be queried by feature vectors created through forward propagating previously-unseen patches through CNN. The proposed framework is able to generate segmentation patches that were not seen during training. Evaluated using publicly available datasets (DRIVE, STARE) demonstrated better performance than state-of-the-art methods in terms of multiple evaluation metrics. The accuracy, robustness, speed and simplicity of the proposed framework demonstrates its suitability for automated vessel segmentation.

Additional Information:Date of Conference: 18-21 July 2018
Keywords:deep learning, retinal imaging, vessel segmentation, medical imaging
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:34085
Deposited On:07 Jan 2019 09:44

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