|
Due to the high intricacy and inter-patient variability of liver vascular anatomy, planning, and execution of liver resection is challenging. Currently, intraoperative ultrasound (IOUS) is an indispensable imaging modality in the surgical workflow; however, 2D-US imaging modality can be difficult to interpret due to noise and speckle. Determining the exact location of tumors and identifying critical structures to preserve during hepatectomy demands expertise and advanced skills. An AI-based model that can help identify vessels (inferior vena cava (IVC), right hepatic vein (RHV), left hepatic vein (LHV), and middle hepatic vein (MHV)) for real-time IOUS navigation can be of immense value. In this research work, we describe our visual saliency approach that integrates attention blocks into a U-Net model for real-time liver vessel segmentation. The IOUS dataset contains video recordings derived from 12 patients, procured during liver surgery. Experiments involve analyzing video frames using a leave-one-out crossvalidation (LOOCV) approach. To maintain objectivity, strict separation is ensured between training and testing subsets to prevent the concurrent inclusion of the same patients. Additionally, to assess model robustness, we kept video data from two distinct patients in the withheld test dataset. Our proposed DL model achieved a mean dice score of 0.88, 0.72, 0.53, and 0.78 for IVC, RHV, MHV, and LHV respectively using the LOOCV approach. In the future, this research will be extended for real-time segmentation of all vasculature in the liver to include portal vein anatomy, followed by the translation of our model in the operation room during surgery.
|
