Banca de QUALIFICAÇÃO: Caio Athayde Neves

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : Caio Athayde Neves
DATE: 27/07/2022
TIME: 08:00
LOCAL: Plataforma Teams
TITLE:

Application of deep learning algorithms in the preoperative evaluation of otologic surgery through medical imaging

KEY WORDS:

Otologic Surgical Procedures; Computed Tomography; Deep learning; Computer-Generated 3D Imaging; Surgical models

PAGES: 25
BIG AREA: Ciências da Saúde
AREA: Medicina
SUMMARY:

Middle- and inner-ear surgery is a vital treatment option in hearing loss, infections, and tumors of the lateral skull base. Segmentation of otologic structures from computed tomography (CT) has many potential applications for improving surgical planning but can be an arduous and time-consuming task. We propose an end-to-end solution for the automated segmentation of temporal bone CT using deep learning algorithms (DL). Using 150 manually segmented CT scans, a comparison of 3 DL architectures (AH-Net, U-Net, ResNet) was conducted to compare Dice coefficient, Hausdorff distance, and speed of segmentation of the inner ear, ossicles, facial nerve and sigmoid sinus. Using AH-Net, the Dice coefficient was 0.91 for the inner ear; 0.85 for the ossicles; 0.75 for the facial nerve; and 0.86 for the sigmoid sinus. The average Hausdorff distance was 0.25, 0.21, 0.24 and 0.45 mm, respectively. Blinded experts assessed the accuracy of both techniques, and there was no statistical difference between the ratings for the two methods (p = 0.93). Objective and subjective assessment confirm good correlation between automated segmentation of otologic structures and manual segmentation performed by a specialist. This end-to-end automated segmentation pipeline can help to advance the systematic application of augmented reality, simulation, and automation in otologic procedures.

 

BANKING MEMBERS:
Presidente - 2292514 - IRUENA MORAES KESSLER
Interno - 3217424 - ANDRE LUIZ LOPES SAMPAIO
Externo à Instituição - MARCIO NAKANISHI