Lesion focused super-resolution

Jin Zhu; Guang Yang; Pietro Lio

doi:10.1117/12.2512576

15 March 2019 Lesion focused super-resolution

Jin Zhu, Guang Yang, Pietro Lio

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109491L (2019) https://doi.org/10.1117/12.2512576
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Jin Zhu, Guang Yang, and Pietro Lio "Lesion focused super-resolution", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491L (15 March 2019); https://doi.org/10.1117/12.2512576

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