The impact of neck tilt on the accuracy of deep learning generated contours for CT images of the head and neck.

ABSTRACT:

Background

Deep learning auto segmentation (DLAS) tools are widely used for radiation therapy planning, yet limited information exists on how patient positioning impacts their performance, particularly in head and neck (HN) computed tomography (CT) imaging.

Purpose

This study investigates the impact of abnormal neck tilt on the accuracy of DLAS-generated contours in HN CT scans. We hypothesize that abnormal positioning degrades auto segmentation performance, particularly for organs at risk (OARs) influenced by cervical spine orientation.

Methods

A total of 35 HN CT scans were retrospectively analyzed. Neck tilt was quantified using principal component analysis of the spinal cord contours from C1 to C4, with patients stratified into normal and abnormal tilt groups base

SUBMITTER: Brooks J 

PROVIDER: S-EPMC12582642 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature