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Lesion focused super-resolution
Author(s): Jin Zhu; Guang Yang; Pietro Lio
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Paper Abstract

Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which has not only resulted in better perceptual image quality without blurring, but also been more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491L (15 March 2019); doi: 10.1117/12.2512576
Show Author Affiliations
Jin Zhu, Univ. of Cambridge (United Kingdom)
Guang Yang, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Pietro Lio, Univ. of Cambridge (United Kingdom)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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