Anatomic Depth Estimation and 3-Dimensional Reconstruction of Microsurgical Anatomy Using Monoscopic High-Definition Photogrammetry and Machine Learning.

Abstract

Immersive anatomic environments offer an alternative when anatomic laboratory access is limited, but current three-dimensional (3D) renderings are not able to simulate the anatomic detail and surgical perspectives needed for microsurgical education. To perform a proof-of-concept study of a novel photogrammetry 3D reconstruction technique, converting high-definition (monoscopic) microsurgical images into a navigable, interactive, immersive anatomy simulation. Images were acquired from cadaveric dissections and from an open-access comprehensive online microsurgical anatomic image database. A pretrained neural network capable of depth estimation from a single image was used to create depth maps (pixelated images containing distance information that could be used for spatial reprojection and 3D rendering). Virtual reality (VR) experience was assessed using a VR headset, and augmented reality was assessed using a quick response code-based application and a tablet camera. Significant correlation was found between processed image depth estimations and neuronavigation-defined coordinates at different levels of magnification. Immersive anatomic models were created from dissection images captured in the authors' laboratory and from images retrieved from the Rhoton Collection. Interactive visualization and magnification allowed multiple perspectives for an enhanced experience in VR. The quick response code offered a convenient method for importing anatomic models into the real world for rehearsal and for comparing other anatomic preparations side by side. This proof-of-concept study validated the use of machine learning to render 3D reconstructions from 2-dimensional microsurgical images through depth estimation. This spatial information can be used to develop convenient, realistic, and immersive anatomy image models.