Automated Extraction of Anatomical Measurements From Temporal Bone CT Imaging. (8th February 2022) - Ding, Andy S.; Lu, Alexander; Li, Zhaoshuo; Galaiya, Deepa; Ishii, Masaru; Siewerdsen, Jeffrey H.; Taylor, Russell H.; Creighton, Francis X.
Abstract : Objective: Proposed methods of minimally invasive and robot‐assisted procedures within the temporal bone require measurements of surgically relevant distances and angles, which often require time‐consuming manual segmentation of preoperative imaging. This study aims to describe an automatic segmentation and measurement extraction pipeline of temporal bone cone‐beam computed tomography (CT) scans. Study Design: Descriptive study of temporal bone measurements. Setting: Academic institution. Methods: A propagation template composed of 16 temporal bone CT scans was formed with relevant anatomical structures and landmarks manually segmented. Next, 52 temporal bone CT scans were autonomously segmented using deformable registration techniques from the Advanced Normalization Tools Python package. Anatomical measurements were extracted via in‐house Python algorithms. Extracted measurements were compared to ground truth values from manual segmentations. Results: Paired t test analyses showed no statistical difference between measurements using this pipeline and ground truth measurements from manually segmented images. Mean (SD) malleus manubrium length was 4.39 (0.34) mm. Mean (SD) incus short and long processes were 2.91 (0.18) mm and 3.53 (0.38) mm, respectively. The mean (SD) maximal diameter of the incus long process was 0.74 (0.17) mm. The first and second facial nerve genus had mean (SD) angles of 68.6 (6.7) degrees and 111.1 (5.3) degrees, respectively. The facial recess had a mean (SD) span of 3.21 (0.46) mm. Mean (SD) minimum distance between the external auditory canal and tegmen was 3.79 (1.05) mm. Conclusions: This is the first study to automatically extract relevant temporal bone anatomical measurements from CT scans using segmentation propagation. Measurements from these models can streamline preoperative planning, improve future segmentation techniques, and help develop future image‐guided or robot‐assisted systems for temporal bone procedures.