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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.
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OBJECTIVE: To assess whether virtual expiratory (VE)-computed tomography (CT)/ultrasound (US) fusion imaging is more effective than conventional inspiratory (CI)-CT/US fusion imaging for hepatic interventional procedures. MATERIALS AND METHODS: This prospective study was approved by the Institutional Review Board, and informed consent was obtained from each patient. In total, 62 patients with focal hepatic lesions referred for hepatic interventional procedures were enrolled. VE-CT images were generated from CI-CT images to reduce the effects of respiration-induced liver motion. The two types of CT images were fused with real-time US images for each patient. The operators scored the visual similarity with the liver anatomy upon initial image fusion and the summative usability of complete image fusion using the respective five-point scales. The time required for complete image fusion and the number of point locks used were also compared. RESULTS: In comparison with CI-CT/US fusion imaging, VE-CT/US fusion imaging showed significantly higher visual similarity with the liver anatomy on the initial image fusion (mean score, 3.9 vs. 1.7; p < 0.001) and higher summative usability for complete image fusion (mean score, 4.0 vs. 1.9; p < 0.001). The required time (mean, 11.1 seconds vs. 22.5 seconds; p < 0.001) and the number of point locks (mean, 1.6 vs. 3.0; p < 0.001) needed for complete image fusion using VE-CT/US fusion imaging were significantly lower than those needed for CI-CT/US fusion imaging. CONCLUSION: VE-CT/US fusion imaging is more effective than CI-CT/US fusion imaging for hepatic interventional procedures.
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Humanos , Comitês de Ética em Pesquisa , Consentimento Livre e Esclarecido , Fígado , Estudos Prospectivos , Respiração , Ultrassonografia , Pesos e MedidasRESUMO
PURPOSE: The purpose of this study was to evaluate the accuracy of an active contour model for estimating the posterior ablative margin in images obtained by the fusion of real-time ultrasonography (US) and 3-dimensional (3D) US or magnetic resonance (MR) images of an experimental tumor model for radiofrequency ablation. METHODS: Chickpeas (n=12) and bovine rump meat (n=12) were used as an experimental tumor model. Grayscale 3D US and T1-weighted MR images were pre-acquired for use as reference datasets. US and MR/3D US fusion was performed for one group (n=4), and US and 3D US fusion only (n=8) was performed for the other group. Half of the models in each group were completely ablated, while the other half were incompletely ablated. Hyperechoic ablation areas were extracted using an active contour model from real-time US images, and the posterior margin of the ablation zone was estimated from the anterior margin. After the experiments, the ablated pieces of bovine rump meat were cut along the electrode path and the cut planes were photographed. The US images with the estimated posterior margin were compared with the photographs and post-ablation MR images. The extracted contours of the ablation zones from 12 US fusion videos and post-ablation MR images were also matched. RESULTS: In the four models fused under real-time US with MR/3D US, compression from the transducer and the insertion of an electrode resulted in misregistration between the real-time US and MR images, making the estimation of the ablation zones less accurate than was achieved through fusion between real-time US and 3D US. Eight of the 12 post-ablation 3D US images were graded as good when compared with the sectioned specimens, and 10 of the 12 were graded as good in a comparison with nicotinamide adenine dinucleotide staining and histopathologic results. CONCLUSION: Estimating the posterior ablative margin using an active contour model is a feasible way of predicting the ablation area, and US/3D US fusion was more accurate than US/MR fusion.
