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Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.

作者信息

Taleb Ali, Leclerc Sarah, Hussein Raabid, Lalande Alain, Bozorg-Grayeli Alexis

机构信息

ICMUB Laboratory UMR CNRS 6302, University of Burgundy Franche Comte, 21000, Dijon, France.

Oticon Medical, 06220, Vallauris, France.

出版信息

Eur Arch Otorhinolaryngol. 2024 Jun;281(6):2921-2930. doi: 10.1007/s00405-023-08403-0. Epub 2024 Jan 10.


DOI:10.1007/s00405-023-08403-0
PMID:38200355
Abstract

PURPOSE: Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery. METHODS: CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances. RESULTS: The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of mm for the incus and mm for the round window. The average Hausdorff distance for these 2 targets was mm and mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s. CONCLUSIONS: A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

摘要

相似文献

[1]
Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.

Eur Arch Otorhinolaryngol. 2024-6

[2]
Augmented Reality of the Middle Ear Combining Otoendoscopy and Temporal Bone Computed Tomography.

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[3]
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[4]
Cadaveric feasibility study of da Vinci Si-assisted cochlear implant with augmented visual navigation for otologic surgery.

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[5]
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[6]
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[7]
A 3D and Explainable Artificial Intelligence Model for Evaluation of Chronic Otitis Media Based on Temporal Bone Computed Tomography: Model Development, Validation, and Clinical Application.

J Med Internet Res. 2024-8-8

[8]
Application of high resolution computed tomography image assisted classification model of middle ear diseases based on 3D-convolutional neural network.

Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022-8-28

[9]
Registration using 3D-printed rigid templates outperforms manually scanned surface matching in image-guided temporal bone surgery.

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[10]
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本文引用的文献

[1]
Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges.

J Clin Med. 2023-8-19

[2]
Vision-Based Augmented Reality System for Middle Ear Surgery: Evaluation in Operating Room Environment.

Otol Neurotol. 2022-3-1

[3]
Video-based augmented reality combining CT-scan and instrument position data to microscope view in middle ear surgery.

Sci Rep. 2020-4-21

[4]
Asymmetrical surface scanning registration for image-guided otologic surgery: A phantom study.

Auris Nasus Larynx. 2020-8

[5]
Noninvasive Registration Strategies and Advanced Image Guidance Technology for Submillimeter Surgical Navigation Accuracy in the Lateral Skull Base.

Otol Neurotol. 2018-12

[6]
Augmented Reality of the Middle Ear Combining Otoendoscopy and Temporal Bone Computed Tomography.

Otol Neurotol. 2018-9

[7]
Surgical anatomy and pathology of the middle ear.

J Anat. 2016-2

[8]
Virtual Endoscopy to Plan Transtympanic Approach to Labyrinthine Windows.

Otol Neurotol. 2015-9

[9]
Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head-and-neck radiotherapy: a simulation study using patient data.

Med Phys. 2014-12

[10]
High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head.

IEEE Trans Biomed Eng. 2013-1-18

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