Deep learning-based automated quantification of the hepatorenal index for evaluation of fatty liver by ultrasonography

Purpose@#The aim of this study was to develop and validate a fully-automatic quantification of the hepatorenal index (HRI) calculated by a deep convolutional neural network (DCNN) comparable to the interpretations of radiologists experienced in ultrasound (US) imaging. @*Methods@#In this retrospective analysis, DCNN-based organ segmentation with Gaussian mixture modeling for automated quantification of the HRI was developed using abdominal US images from a previous study. For validation, 294 patients who underwent abdominal US examination before living-donor liver transplantation were selected. Interobserver agreement for the measured brightness of the liver and kidney and the calculated HRI were analyzed between two board-certified radiologists and DCNN using intraclass correlation coefficients (ICCs). @*Results@#Most patients had normal (n=95) or mild (n=198) fatty liver. The ICCs of hepatic and renal brightness measurements and the calculated HRI between the two radiologists were 0.892 (95% confidence interval [CI], 0.866 to 0.913), 0.898 (95% CI, 0.873 to 0.918), and 0.681 (95% CI, 0.615 to 0.738) for the first session and 0.920 (95% CI, 0.901 to 0.936), 0.874 (95% CI, 0.844 to 0.898), and 0.579 (95% CI, 0.497 to 0.650) for the second session, respectively; the results ranged from moderate to excellent agreement. Using the same task, the ICCs of the hepatic and renal measurements and the calculated HRI between the average values of the two radiologists and DCNN were 0.919 (95% CI, 0.899 to 0.935), 0.916 (95% CI, 0.895 to 0.932), and 0.734 (95% CI, 0.676 to 0.782), respectively, showing high to excellent agreement. @*Conclusion@#Automated quantification of HRI using DCNN can yield HRI measurements similar to those obtained by experienced radiologists in patients with normal or mild fatty liver.

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.