1. A generalized approach for automatic 3-D geometry assessment of blood vessels in transverse ultrasound images using convolutional neural networks
- Author
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Richard G.P. Lopata, Marc R.H.M. van Sambeek, Joerik de Ruijter, Judith Muijsers, Frans N. van de Vosse, Eindhoven MedTech Innovation Center, Photoacoustics & Ultrasound Laboratory Ehv, Cardiovascular Biomechanics, Biomedical Engineering, and EAISI Health
- Subjects
Acoustics and Ultrasonics ,Neural Networks ,Computer science ,Image Processing ,External carotid artery ,Convolutional neural network ,Veins ,Machine Learning ,Computer ,Computer-Assisted ,Biomedical imaging ,Robustness (computer science) ,medicine.artery ,medicine ,Medical imaging ,Image Processing, Computer-Assisted ,Segmentation ,Electrical and Electronic Engineering ,vascular ultrasound (US) ,Carotid Arteries/diagnostic imaging ,Instrumentation ,Vascular Ultrasound ,Ultrasonography ,Image segmentation ,medical image segmentation ,Training data ,Artificial neural network ,business.industry ,Ultrasonic imaging ,Pattern recognition ,Carotid arteries ,Probes ,Artificial intelligence ,Neural Networks, Computer ,Focus (optics) ,business - Abstract
Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Convolutional neural networks are a promising tool for automatic segmentation of medical images, outperforming the traditional segmentation methods with high robustness. In this study, we aim to create a general, robust, and accurate method to segment the lumen–wall boundary of healthy central and peripheral vessels in large field-of-view freehand ultrasound (US) datasets. Data were acquired using the freehand US, in combination with a probe tracker. A total of ±36 000 cross-sectional images, acquired in the common, internal, and external carotid artery ( ${N} = 37$ ), in the radial, ulnar artery, and cephalic vein ( ${N} = 12$ ), and in the femoral artery ( ${N} = 5$ ) were included. To create masks (of the lumen) for training data, a conventional automatic segmentation method was used. The neural networks were trained on: 1) data of all vessels and 2) the carotid artery only. The performance was compared and tested using an open-access dataset. The recall, precision, DICE, and intersection over union (IoU) were calculated. Overall, segmentation was successful in the carotid and peripheral arteries. The Multires U-net architecture performs best overall with DICE = 0.93 when trained on the total dataset. Future studies will focus on the inclusion of vascular pathologies.
- Published
- 2021