Development of novel algorithm to visualize blood vessels on 3D ultrasound images during liver surgery
Fatemeh Salehihafshejani, Alireza Ahmadian, Afshin Shoeibi, Roohallah, Alizadehsani, Habibollah Dashti, Niloofar Ayoobi Yazdi, Abbas Khosravi, Saeid, Nahavandi

TL;DR
This paper introduces a new algorithm for 3D visualization of blood vessels in noisy liver ultrasound images to assist surgeons and reduce errors during liver surgery.
Contribution
A novel volume visualization algorithm tailored for noisy liver ultrasound images, enhancing vessel and tumor delineation for surgical guidance.
Findings
Effective 3D visualization of liver vessels achieved
Improved delineation of vessels and tumors in ultrasound images
Potential to reduce surgical errors during liver procedures
Abstract
Volume visualization is a method that displays three-dimensional (3D) data in two-dimensional (2D) space. Using 3D datasets instead of 2D traditional images improves the visualization of anatomical structures, and volume visualization helps radiologists and surgeons to review large datasets comprehensively so that diagnosis and treatment can be enhanced. In liver surgery, blood vessel detection is important. Liver vessels have various shapes and due to the presence of noise in the ultrasound images, they can be confused with noise. Suboptimal images can sometimes lead to surgical errors where the surgeon may cut the blood vessel in error. The ultrasound system is versatile and portable and has the advantage of being able to be used in the operating theatre. Due to the nature of B-mode ultrasound, 1-D transfer function volume visualization of images cannot abrogate shadow artifacts.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRadiation Dose and Imaging · Pancreatic and Hepatic Oncology Research · MRI in cancer diagnosis
