Depth based registration of 3D preoperative models to intraoperative patient anatomy using the HoloLens 2

Purpose: Image-guided surgery can decrease perioperative complication incidences. Augmented Reality (AR) with head-mounted displays (HMDs) provides an accessible method of visualizing 3D preoperative models intraoperatively. This could provide the surgeon with an easy to use intraoperative image-guided surgery system. Image-to-patient...

Segmentation of Deep Inferior Epigastric Artery Perforators for Breast Reconstruction using Deep Learning in Computed Tomography Angiography: A Quantitative and Qualitative Evaluation

<b>Introduction: </b>Breast reconstruction after a mastectomy is crucial to improve a patient’s quality of life. The Deep Inferior Epigastric artery Perforator (DIEP) flap procedure is considered the golden standard for breast reconstruction. Three-dimensional (3D) visualization methods have shown promise in providing a better understanding of...

Utilising Deep Learning Models for the Surface Registration Problem in HoloNav

Surface Registration is a registration problem that handles the registration of two similar surfaces. In most research that utilises Deep Learning (DL) models to handle surface registration two theories are investigated; the first being whether surfaces sampled from the same origin can be registered together, and the second theory being whether...

Performing patient alignment utilizing point-cloud surface registration techniques in HoloNav

In order to be able to use the Microsoft HoloLens for surgical navigation purposes, performing good patient alignment is of utmost importance. This paper will discuss how this patient alignment can be done using different point cloud registration algorithms.<br/>A lot of research is being conducted on point cloud registration algorithms. However...