University of Twente Student Theses

Login

Automated vascular region segmentation in ultrasound to utilize surgical navigation in liver surgery

Thomson, B.R. (2019) Automated vascular region segmentation in ultrasound to utilize surgical navigation in liver surgery.

[img] PDF
5MB
Abstract:The liver is a common location for primary cancer and metastatic disease. Currently, ultrasound is the only imaging modality that is widely accepted and integrated into a surgical workflow, automatic registration with preoperative imaging would provide great value in determining a resection plan. An initial registration is performed based on the ultrasound probe orientation and one point translation. The centerline of automatically, using a 3D U-Net, segmented intraoperative ultrasound is registered with the preoperative vasculature model. In visually successful registrations we acquire a target registration error of 12.29 ( +/- 4.93 mm), however, 55 % of the registrations fail expectantly due to a relatively big volume difference with respect to the ultrasound information that is acquired. Manually adjusting these cropped volumes reduces the TREs over all volumes from 47.32 (+/- 25.71 mm) to 25.66 (+/- 10.48 mm). In conclusion, we demonstrate a fast (69.74 +/- 14.6 seconds) deep learning based hepatic vasculature registration pipeline. Given that the ultrasound acquisitions do not contain the vena cava or gallbladder, and span a large part of the hepatic vasculature, our approach looks promising. Further optimization of automatically acquiring similar point clouds is expected to stimulate the adaptation of surgical navigation.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine, 54 computer science
Programme:Technical Medicine MSc (60033)
Link to this item:https://purl.utwente.nl/essays/79399
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page