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Objectives To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. Methods In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). Results We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. Conclusions The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. Key Points • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

Small RHs in peripheral areas may require aggressive treatment because they respond well to treatment. In larger RHs, staged treatment could reduce treatment-related complications. Transpupillary thermotherapy could be an effective method in tumor regression for moderate-to-large-sized RHs showing tumor regression rate of 70%.

PurposeTo assess efficacy of the Pentacam (PTC) and the anterior segment optical coherence tomography (AOCT) for detection of occludable angles.Materials and MethodsFourty-one eyes with gonioscopically diagnosed occludable angles and 32 normal open-angle eyes were included. Anterior chamber angle (ACA) and anterior chamber depth (ACD) were measured with PTC and AOCT. Receiver operating characteristic (ROC) curve was constructed for each parameter and the area under the ROC curve (AUC) was calculated.ResultsValues of ACA and ACD measured by PTC and AOCT were similar not only in normal open angle eyes but also in occludable angle eyes. For detection of occludable angle, the AUCs of PTC with ACA and ACD were 0.935 and 0.969, respectively. The AUCs of AOCT with ACA and ACD were 0.904 and 0.947, respectively.ConclusionBoth PTC and AOCT allow accurate discrimination between open and occludable angle eyes, so that they may aid to screening the occludable angles.

Reviews of the most recent applications of deep learning on ultrasound imaging applications are presented. Architectures of deep learning networks are briefly explained for medical imaging application categories of classification, detection, segmentation, and generation. Ultrasonography applications are then reviewed and summarized for image processing and diagnosis along with some representative study cases of breast, thyroid, heart, kidney, liver, and fetal head. Efforts on workflow enhancement are also reviewed with emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented on image quality Enhancement, diagnostic support, and improving workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

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.

Intraoperative adjustment was effective in concomitant horizontal strabismus surgery and can provide the opportunity to avoid a large overcorrection, especially in cases with moderate angle horizontal muscle surgery.