Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning
| Autor: | Naoki Takahashi, Alexander D. Weston, Bradley J. Erickson, Panagiotis Korfiatis, Petro M. Kostandy, Timothy L. Kline, Tomas Sakinis, Motokazu Sugimoto, Kenneth A. Philbrick |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Radiography Abdominal Carcinoma Hepatocellular Radiography Standard deviation Pattern Recognition Automated 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake Deep Learning 0302 clinical medicine medicine Humans Radiology Nuclear Medicine and imaging Segmentation Aged Retrospective Studies Aged 80 and over business.industry Liver Neoplasms Middle Aged Data set medicine.anatomical_structure Bonferroni correction 030220 oncology & carcinogenesis Test set Body Composition symbols Radiographic Image Interpretation Computer-Assisted Abdomen Tomography Tomography X-Ray Computed business Nuclear medicine Algorithms |
| Zdroj: | Radiology. 290:669-679 |
| ISSN: | 1527-1315 0033-8419 |
| Popis: | Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC). CT examinations were performed between 1997 and 2015. The mean age of patients was 67 years; for male patients, it was 67 years (range, 29-94 years), and for female patients, it was 66 years (range, 31-97 years). Differences in segmentation performance were assessed by using two-way analysis of variance with Bonferroni correction. Results Compared with reference segmentation, the model for this study achieved Dice scores (mean ± standard deviation) of 0.98 ± 0.03, 0.96 ± 0.02, and 0.97 ± 0.01 in the test set, and 0.94 ± 0.05, 0.92 ± 0.04, and 0.98 ± 0.02 in the HCC data set, for the subcutaneous, muscle, and visceral adipose tissue compartments, respectively. Performance met or exceeded that of expert manual segmentation. Conclusion Model performance met or exceeded the accuracy of expert manual segmentation of CT examinations for both the test data set and the hepatocellular carcinoma data set. The model generalized well to multiple levels of the abdomen and may be capable of fully automated quantification of body composition metrics in three-dimensional CT examinations. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Chang in this issue. |
| Databáze: | OpenAIRE |
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