Computer Vision News - May 2022

53 Laparoscopy Fusion (P2ILF) learning rather than a mathematical model. The challenge uses single image frames, but he does not rule out using video in future editions. The plan is first to assess where the community is at in solving this kind of complex problem in the era of deep learning. “ I don’t want to scare people by saying it’s complex, ” Sharib interjects. “ The only way to approach it is to truly understand the problem and what you’re trying to achieve. There are two tasks, but the registration task is particularly important and can be challengingwith liver views at different positions and angles. People must take great care when finding the landmarks and understand which Augmented reality-assisted laparoscopic liver surgery uses key landmark detection from intraoperative 2D video frames registered to a preoperative 3D livermodel from CT/MRI data for tumor localization. “ Sometimes tumors are not visible in laparoscopy images because they’re embedded inside the liver, ” Sharib tells us. “ However, these tumors can be visible in preoperative CT/MRI scans, so the idea is to fuse the two in real-time to give the precise location of the tumor during surgery . This allows the surgeon to resect it completely, reducing the risk of recurrence and ultimately saving lives. ” The P2ILF challenge asks participants to use machine learning methods for two tasks. They must first segment five liver anatomical curves , including silhouette, falciform ligament, left and right ridges, and liver boundary, from the 2D intraoperative images and preoperative 3D liver model. The second task is to register those segmented curves in 2D laparoscopy to the corresponding landmarks in the 3D model. Most current methods use traditional computer vision methodologies to do this. Sharib approached Adrien Bartoli ,ProfessorofComputer Science at Clermont Auvergne University, and discussed designing tasks using deep