A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.
Otolaryngol Head Neck Surg
; 169(4): 988-998, 2023 10.
Article
em En
| MEDLINE
| ID: mdl-36883992
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. STUDYDESIGN:
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
Orelha Interna
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article