To investigate the feasibility of a deep learning method based on a UNETR model for fully automatic segmentation of the cochlea in temporal bone CT images.
The normal temporal bone CTs of 77 patients were used in 3D U-Net and UNETR model automatic cochlear segmentation. Tests were performed on two types of CT datasets and cochlear deformity datasets.
Through training the UNETR model, when batch_size=1, the Dice coefficient of the normal cochlear test set was 0.92, which was higher than that of the 3D U-Net model; on the GE 256 CT, SE-DS CT and Cochlear Deformity CT dataset tests, the Dice coefficients were 0.91, 0.93, 0 93, respectively.
According to the anatomical characteristics of the temporal bone, the use of the UNETR model can achieve fully automatic segmentation of the cochlea and obtain an accuracy close to manual segmentation. This method is feasible and has high accuracy.
Abbreviations:CT (computed tomography), IP (Incomplete partition), SNHL (sensorineural hearing loss), CIs (cochlear implants), MRI (magnetic resonance imaging), AI (artificial intelligence), DL (deep learning)
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Auris Nasus Larynx
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Pediatric sensorineural hearing loss, part 1: practical aspects for neuroradiologists.AJNR Am J Neuroradiol. 2012; 33: 211-217
- Audiologic and radiologic findings in cochlear hypoplasia.Auris Nasus Larynx. 2017; 44: 655-663
- Unpartitioned versus incompletely partitioned cochleae: radiologic differentiation.Otol Neurotol. 2004; 25 (discussion 529): 520-529
- A new classification for cochleovestibular malformations.Laryngoscope. 2002; 112: 2230-2241
- Cochlear implants in adults and children.NIH Consens Statement. 1995; 13: 1-30
- The mondini dysplasia-from early diagnosis to cochlear implant[J].Acta Otolaryngol. 1983; 95: 627-631
- Clinical outcomes following cochlear implantation in children with inner ear anomalies.Int J Pediatr Otorhinolaryngol. 2017; 93: 1-6
- Real-time intraoperative computed tomography to assist cochlear implant placement in the malformed inner ear.Otol Neurotol. 2009; 30: 23-26
- Cochlear length determination using cone beam computed tomography in a clinical setting.Hear Res. 2014; 316: 65-72
- Impact of electrode insertion depth on intracochlear trauma.Otolaryngol Head Neck Surg. 2006; 135: 374-382
- Cochlear implantation in inner ear malformations–a review article.Cochlear Implant Int. 2010; 11: 4-41
- Sensorineural and conductive hearing loss associated with lateral semicircular canal malformation.Laryngoscope. 2000; 110: 1673-1679
- Robotic middle ear access for cochlear implantation: first in man.PLoS One. 2019; 14e0220543
- Instrument flight to the inner ear.Sci Robot. 2017; 2: 1-12
- Robotic cochlear implant surgery: imaging-based evaluation of feasibility in clinical routine.Front Surg. 2021; 8742219
- Multi-scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.Comput Methods Programs Biomed. 2020; 191105387
- A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images.Neural Netw. 2020; 124: 75-85
- Toward an automatic preoperative pipeline for image-guided temporal bone surgery.Int J Comput Assist Radiol Surg. 2019; 14: 967-976
- Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery.Int J Comput Assist Radiol Surg. 2020; 15: 1825-1833
- Automated measurement of hydrops ratio from MRI in patients with Meniere's disease using CNN-based segmentation.Sci Rep. 2020; 10: 7003
- Automatic detection of the inner ears in head CT images using deep convolutional neural networks.Proc SPIE Int Soc Opt Eng. 2018; 105741057427
- PWD-3DNet: a deep learning-based fully-automated segmentation of multiple structures on temporal bone CT scans.IEEE Trans Image Process. 2021; 30: 739-753
- Automatic semicircular canal segmentation of CT volumes using improved 3D U-net with attention mechanism.Comput Intell Neurosci. 2021; 20219654059
- Automatic segmentation of temporal bone structures from clinical conventional CT using a CNN approach.Int J Med Robot. 2021; 17: e2229
- Fully automated segmentation in temporal bone CT with neural network: a preliminary assessment study.BMC Med Imaging. 2021; 21: 166
- Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network.Sci Rep. 2021; 11: 4406
- Deep learning for the fully automated segmentation of the inner ear on MRI.Sci Rep. 2021; 11: 2885
- 3D U-Net: learning dense volumetric segmentation from sparse annotation.(ed.)in: Ourselin S Joskowicz L Sabuncu M Unal G Wells W Medical image computing and computer-assisted intervention, lecture notes in computer science. Springer, Cham, Switzerland2016: 424-432
Ali H, Yucheng T, Vishwesh N, Dong Y, Andriy M, Bennett L, et al. UNETR: transformers for 3D medical image segmentation. arXiv preprint arXiv:2103.10504, 2021.
- Classification and current management of inner ear malformations.Balkan Med J. 2017; 34: 397-411
- Augmented reality for inner ear procedures: visualization of the cochlear central axis in microscopic videos.Int J Comput Assist Radiol Surg. 2020; 15: 1703-1711
Published online: August 12, 2022
Accepted: June 30, 2022
Received: April 24, 2022
© 2022 Japanese Society of Otorhinolaryngology-Head and Neck Surgery, Inc. Published by Elsevier B.V. All rights reserved.