Automated Estimation of Anatomical Risk Metrics for Endoscopic Sinus Surgery Using Deep Learning

TL;DR

This paper introduces a deep learning method to automatically estimate anatomical risk scores for endoscopic sinus surgery, reducing manual measurement effort and improving preoperative assessment accuracy.

Contribution

The study presents a novel automated pipeline using heatmap regression for localizing landmarks and estimating risk scores, outperforming direct approaches.

Findings

Mean absolute error of 0.506mm for Keros score

Mean absolute error of 4.516° for Gera score

Accurate classification of TMS with errors around 0.8mm

Abstract

Endoscopic sinus surgery requires careful preoperative assessment of the skull base anatomy to minimize risks such as cerebrospinal fluid leakage. Anatomical risk scores like the Keros, Gera and Thailand-Malaysia-Singapore score offer a standardized approach but require time-consuming manual measurements on coronal CT or CBCT scans. We propose an automated deep learning pipeline that estimates these risk scores by localizing key anatomical landmarks via heatmap regression. We compare a direct approach to a specialized global-to-local learning strategy and find mean absolute errors on the relevant anatomical measurements of 0.506mm for the Keros, 4.516{\deg} for the Gera and 0.802mm / 0.777mm for the TMS classification.