Advanced Biomedical Engineering
Online ISSN : 2187-5219
ISSN-L : 2187-5219
Segmentation of Liver Blood Vessel in Ultrasound Images Using Mask R-CNN
Koki TANAKATakeru KURIHARAYukino TAKAHASHIShinya ONOGITakaaki SUGINOYoshikazu NAKAJIMAYoshihiro EDAMOTOKohji MASUDA
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JOURNAL OPEN ACCESS

2024 Volume 13 Pages 379-388

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Abstract

To realize real-time image registration between preoperative three-dimensional ultrasound images and an intraoperative two-dimensional (2D) ultrasound image, accurate image extraction of vascular networks at a high frame rate is necessary. To apply this approach to liver surgery, we attempted to modify the parameters of the Mask region-based convolutional neural network (R-CNN) deep learning model to extract liver blood vessels. The acquired 2D ultrasound images of the liver were divided into training and evaluation datasets, and a model was built using the training dataset. We modified the components of feature extraction, region proposal, and mask detection in the fundamental architecture of the Mask R-CNN. Finally, the model constructed was compared with the conventional Mask R-CNN using the hold-out method. The results revealed improvements of the dice similarity coefficient and the computational time. The findings suggest that the modified Mask R-CNN can be employed for highly accurate real-time detection of liver blood vessels.

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© 2024 Japanese Society for Medical and Biological Engineering

Copyright: ©2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons BY 4.0 International (Attribution) License (https://creativecommons.org/licenses/by/4.0/legalcode), which permits the unrestricted distribution, reproduction and use of the article provided the original source and authors are credited.
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