How to overcome the data limited segmentation in abdominal CT: multi-planar UNet coupled with augmented contrast-boosting

Sihwan Kim; Chulkyun Ahn; Jong Hyo Kim

doi:10.1117/12.2654171

3 April 2023 How to overcome the data limited segmentation in abdominal CT: multi-planar UNet coupled with augmented contrast-boosting

Sihwan Kim, Chulkyun Ahn, Jong Hyo Kim

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124642S (2023) https://doi.org/10.1117/12.2654171
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The quantity and variety of CT imaging data are essential components for effective AI-model training. However, the availability of high-quality CT images for organ segmentation is quite constrained, and the AI-based organ segmentation could be impacted by the varying intensity of contrast agents. Therefore, to improve the robustness of the segmentation both with and without a contrast agent, as well as to solve the data shortage issue, we proposed a multi-planar UNet with an augmented contrast-boosting technique. Any program employing the proposed method may see greater benefits from reducing the burden of large-scale dataset preparation, improving the AI-model training efficiency.

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Sihwan Kim, Chulkyun Ahn, and Jong Hyo Kim "How to overcome the data limited segmentation in abdominal CT: multi-planar UNet coupled with augmented contrast-boosting", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124642S (3 April 2023); https://doi.org/10.1117/12.2654171

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