Deep learning-based all-in-one combined visualization strategy for disease screening in CT imaging

Hao Zhou; Jingyi Liao; Zhixiong Zeng; Danyang Li; Yongbo Wang; Zhaoying Bian; Dong Zeng; Jianhua Ma

doi:10.1117/12.2654188

7 April 2023 Deep learning-based all-in-one combined visualization strategy for disease screening in CT imaging

Hao Zhou, Jingyi Liao, Zhixiong Zeng, Danyang Li, Yongbo Wang, Zhaoying Bian, Dong Zeng, Jianhua Ma

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124633B (2023) https://doi.org/10.1117/12.2654188
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Computed tomography (CT) is a widely used medical imaging modality which is capable of displaying the fine details of human body. In clinics, the CT images need to highlight different desired details or structures with different filter kernels and different display windows. To achieve this goal, in this work, we proposed a deep learning based ”All-in-One” (DAIO) combined visualization strategy for high-performance disease screening in the disease screening task. Specifically, the presented DAIO method takes into consideration of both kernel conversion and display window mapping in the deep learning network. First, the sharp kernel, smooth kernel reconstructed images and lung mask are collected for network training. Then, the structure is adaptively transferred to the kernel style through local kernel conversion to make the image have higher diagnostic value. Finally, the dynamic range of the image is compressed to a limited gray level by the mapping operator based on the traditional window settings. Moreover, to promote the structure details enhancement, we introduce a weighted mean filtering loss function. In the experiment, nine of the ten full dose patients cases from the Mayo clinic dataset are utilized to train the presented DAIO method, and one patient case from the Mayo clinic dataset are used for test. Results shows that the proposed DAIO method can merge multiple kernels and multiple window settings into a single one for the disease screening.

Citation Download Citation

Hao Zhou, Jingyi Liao, Zhixiong Zeng, Danyang Li, Yongbo Wang, Zhaoying Bian, Dong Zeng, and Jianhua Ma "Deep learning-based all-in-one combined visualization strategy for disease screening in CT imaging", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124633B (7 April 2023); https://doi.org/10.1117/12.2654188

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