Software that combines deep learning, 3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging.

BACKGROUND: Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease. OBJECTIVE: This paper presents software methodology that can be used in combination with this imaging technique to provide additional information about the state of patient-specific artery. METHODS: Overall three modules are combined within the proposed methodology. A clinical dataset is used within the deep learning module to extract the contours of the carotid artery. This data is then used within the second module to perform the three-dimensional reconstruction of the geometry of the carotid bifurcation and ultimately this geometry is used within the third module, where the hemodynamic analysis is performed. The obtained distributions of hemodynamic quantities enable a more detailed analysis of the blood flow and state of the arterial wall and could be useful to predict further progress of present abnormalities in the carotid bifurcation. RESULTS: The performance of the deep learning module was demonstrated through the high values of relevant common classification metric parameters. Also, the accuracy of the proposed methodology was shown through the validation of results for the reconstructed parameters against the clinically measured values. CONCLUSION: The presented methodology could be used in combination with standard clinical ultrasound examination to quickly provide additional quantitative and qualitative information about the state of the patient's carotid bifurcation and thus ensure a treatment that is more adapted to the specific patient.

Application of Machine Learning for Segmentation of the Pulmonary Acinus Imaged by Synchrotron X-Ray Tomography.

Background: To assess the effectiveness of inhalation therapy, it is important to evaluate the lungs' structure; thus, visualization of the entire lungs at the level of the alveoli is necessary. To achieve this goal, the applied visualization technique must satisfy the following two conditions simultaneously: (1) it has to obtain images of the entire lungs, since one part of the lungs is influenced by the other parts, and (2) the images have to capture the detailed structure of the alveolus/acinus in which gas exchange occurs. However, current visualization techniques do not fulfill these two conditions simultaneously. Segmentation is a process in which each pixel of the obtained high-resolution images is simplified (i.e., the representation of an image is changed by categorizing and modifying each pixel) so that we can perform three-dimensional volume rendering. One of the bottlenecks of current approaches is that the accuracy of the segmentation of each image has to be evaluated on the outcome of the process (mainly by an expert). It is a formidable task to evaluate the astronomically large numbers of images that would be required to resolve the entire lungs in high resolution. Methods: To overcome this challenge, we propose a new approach based on machine learning (ML) techniques for the validation step. Results: We demonstrate the accuracy of the segmentation process itself by comparison with previously validated images. In this ML approach, to achieve a reasonable accuracy, millions/billions of parameters used for segmentation have to be optimized. This computationally demanding new approach is achievable only due to recent dramatic increases in computation power. Conclusion: The objective of this article is to explain the advantages of ML over the classical approach for acinar imaging.