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International Journal of Computer Assisted Radiology and Surgery

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01-11-2020 | Artificial Intelligence | Original Article

Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques

Authors: S. Jeevakala, C. Sreelakshmi, Keerthi Ram, Rajeswaram Rangasami, Mohanasankar Sivaprakasam

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2020

Abstract

Purpose

Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital features without human intervention. The automatic detection and segmentation of subtle tissue such as the internal auditory canal (IAC) and its nerves is a challenging task, and it can be improved using deep learning techniques.

Methods

The main scope of this research is to present an automatic method to detect and segment the IAC and its nerves like the facial nerve, cochlear nerve, inferior vestibular nerve, and superior vestibular nerve. To address this issue, we propose a Mask R-CNN approach driven with U-net to detect and segment the IAC and its nerves. The Mask R-CNN with its backbone network of the RESNET50 model learns a background-based localization policy to produce an actual bounding box of the IAC. Furthermore, the U-net segments the structure related information of IAC and its nerves by learning its features.

Results

The proposed method was experimented on clinical datasets of 50 different patients including adults and children. The localization of IAC using Mask R-CNN was evaluated using Intersection of Union (IoU), and segmentation of IAC and its nerves was evaluated using Dice similarity coefficient.

Conclusions

The localization result shows that mean IoU of RESNET50, RESNET101 are 0.79 and 0.74, respectively. The Dice similarity coefficient of IAC and its nerves using region growing, PSO and U-net method scored 92%, 94%, and 96%, respectively. The result shows that the proposed method outperform better in localization and segmentation of IAC and its nerves. Thus, AI aids the radiologists in making the right decisions as the localization and segmentation of IAC is accurate.

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Title: Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques
Authors: S. Jeevakala
C. Sreelakshmi
Keerthi Ram
Rajeswaram Rangasami
Mohanasankar Sivaprakasam
Publication date: 01-11-2020
Publisher: Springer International Publishing
Keyword: Artificial Intelligence
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2020
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02237-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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