Intraoperative Absolute Depth Estimation in MVD Surgery

EasyChair Preprint 16008

Abstract

Microvascular decompression (MVD) is a neurosurgical procedure that relieves nerve compression by repositioning or separating offending blood vessels, effectively reducing pain or spasms. Accurate localization of the compression site is crucial for optimal surgical outcomes, as it enables precise identification and decompression of the offending vessel. While horizontal anatomical relationships are easily identified in the surgical view, compressions occurring along the depth axis are more challenging to discern. In this study, we propose a method to measure accurate intraoperative distances during MVD surgery using Depth-Anything-V2. By leveraging the optical properties of standard imaging equipment in conjunction with the depth estimation model, our method computes precise, absolute distances rather than relying solely on relative measurements, achieving distance estimation errors of less than 2 mm compared to intraoperative and preoperative reference measurements.

Keyphrases: Microvascular Decompression (MVD), Surgical Video Analysis, deep learning, depth estimation

@booklet{EasyChair:16008,
  author    = {Jinhee Lee and Hwanhee Lee and Jay Park and Ethan Htun and Bruce Xu and Sang Hoon Cho and Sanghoon Lee and Vivek Buch},
  title     = {Intraoperative Absolute Depth Estimation in MVD Surgery},
  howpublished = {EasyChair Preprint 16008},
  year      = {EasyChair, 2026}}