A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging

Abstract

Objective: Preoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time-consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot-assisted procedures in this space. This study evaluates a state-of-the-art deep learning pipeline for semantic segmentation of temporal bone anatomy.

Study design: A descriptive study of a segmentation network.

Setting: Academic institution.

Methods: A total of 15 high-resolution cone-beam temporal bone computed tomography (CT) data sets were included in this study. All images were co-registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U-Net (nnU-Net), an open-source 3-dimensional semantic segmentation neural network, were compared against ground-truth segmentations using modified Hausdorff distances (mHD) and Dice scores.

Results: Fivefold cross-validation with nnU-Net between predicted and ground-truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas-based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).

Conclusion: Using an open-source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand-segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot-assisted systems for the temporal bone.

Keywords: automated segmentation; deep learning; neural network; temporal bone.

Figure 1.

Visual comparison between ground-truth (right) and predicted (left) segmentations from a representative data set. (A) Lateral view. (B) View of the facial recess. (C) View of the middle cranial fossa. Segment IDs are presented for clarity.

Figure 2.

No new U-Net (nnU-Net) and segmentation propagation (Seg Prop) predictions. (A) Dice scores with an accuracy threshold (dotted line) of 0.75. (B) Modified Hausdorff distances with an accuracy threshold (dotted line) of 1 mm. p values: *<.05; **<.01; ***<.001; ****<.0001.

Figure 3.

Accuracy comparisons for smaller/thinner (unshaded) versus larger/thicker (shaded) structures. Constrained linear regressions show the effects of modified Hausdorff distance on Dice score.

Figure 4.

Example of Dice score insensitivity to shape variation and islands. While example (A) produces an inferior vestibular nerve prediction with a distant island and example (B) does not, they maintain similar Dice scores but significantly different modified Hausdorff distances (mHDs).