U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images

Zhongshu Zheng; Ling Ma; Songxiao Yang; Said Boumaraf; Xiabi Liu; Xiaohong Ma

doi:10.1117/12.2580805

15 February 2021 U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images

Zhongshu Zheng, Ling Ma, Songxiao Yang, Said Boumaraf, Xiabi Liu, Xiaohong Ma

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962O (2021) https://doi.org/10.1117/12.2580805
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The contrast enhancement of tumor regions in medical images can improve the performance of tumor detection, segmentation, and diagnosis. However, the main existing enhancement methods aim to enhance the contrast and the resolution on the whole image, instead of highlighting the lesion regions. The blurry edges lead to the difficulty of distinguishing the tumor from the healthy tissues accurately. This issue can be hardly solved by those global enhancement methods. In this paper, we focus on the local enhancement and propose a novel deep learning-based approach called U-SDRC to enhance the contrast between tumor regions and surrounding background tissues to make the tumor regions distinguishable. We introduce a U-net deep network to tackle this problem and present a novel SDRC loss function to achieve the goal of enhancing tumor lesions and simultaneously preserving the original appearance of other regions in the image. We evaluate our approach on a clinical dataset that comprises 1394 liver CT slices. The encouraging experimental results show that the proposed method can lead to a good visual enhancement effect and bring improvements to medical tasks such as tumor segmentation and diagnosis.

Conference Presentation

Citation Download Citation

Zhongshu Zheng, Ling Ma, Songxiao Yang, Said Boumaraf, Xiabi Liu, and Xiaohong Ma "U-SDRC: a novel deep learning-based method for lesion enhancement in liver CT images", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962O (15 February 2021); https://doi.org/10.1117/12.2580805

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