Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Korean Journal of Radiology : Official Journal of the Korean Radiological Society
Yura AhnHo Sung Kim

Abstract

Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. In test dataset-1, the DLA achieved...Continue Reading

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Citations

Mar 13, 2021·Journal of Gastroenterology and Hepatology·Tung-Hung SuJia-Horng Kao
Mar 13, 2021·Journal of Gastroenterology and Hepatology·Yu Sub SungSeung Soo Lee
Jun 15, 2021·Frontiers in Oncology·Yong TangSong Su
Jun 17, 2021·Seminars in Ultrasound, CT, and MR·Mayur VirarkarPriya Bhosale
Aug 19, 2021·The American Journal of Cardiology·Atsushi SugiuraAlexander Sedaghat

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Software Mentioned

AsanJ
ImageJ
SPSS
DeepLabV3

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