Koki TANAKA, Takeru KURIHARA, Yukino TAKAHASHI, Shinya ONOGI, Takaaki SUGINO, Yoshikazu NAKAJIMA, Yoshihiro EDAMOTO, Kohji MASUDA
Vol. 13 (2024) p. 379-388

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.