Image Processing Pipeline for Liver Fibrosis Classification Using Ultrasound Shear Wave Elastography

• Published in: Ultrasound in medicine & biology Vol. 46; no. 10; pp. 2667 - 2676
• Main Authors: Brattain, Laura J., Ozturk, Arinc, Telfer, Brian A., Dhyani, Manish, Grajo, Joseph R., Samir, Anthony E.
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.10.2020
• Subjects: Liver fibrosis; Machine learning; Multi-image classification; Radiology; Shear wave elastography; Single-image classification; Multi-image classification; Shear wave elastography; Liver fibrosis; Machine learning; Single-image classification
• ISSN: 0301-5629; 1879-291X; 1879-291X
• DOI: 10.1016/j.ultrasmedbio.2020.05.016

The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each of eight or more SWE images, computing the mean tissue stiffness within each of the regions of interest and computing a resulting stiffness value as the median of the means. The 527-subject database consisted of 5526 SWE images and pathologist-scored biopsies, with data collected from a single system at a single site. The automated method integrates three modules that assess SWE image quality, select a region of interest from each SWE measurement and perform machine learning-based, multi-image SWE classification for fibrosis stage ≥F2. Several classification methods were developed and tested using fivefold cross-validation with training, validation and test sets partitioned by subject. Performance metrics were area under receiver operating characteristic curve (AUROC), specificity at 95% sensitivity and number of SWE images required. The final automated method yielded an AUROC of 0.93 (95% confidence interval: 0.90–0.94) versus 0.69 (95% confidence interval: 0.65–0.72) for the reference method, 71% specificity with 95% sensitivity versus 5% and four images per decision versus eight or more. In conclusion, the automated method reported in this study significantly improved the accuracy for ≥F2 classification of SWE measurements as well as reduced the number of measurements needed, which has the potential to reduce clinical workflow.