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.

中文翻译：

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人工智能使用深度学习技术检测和分割内耳道及其神经。

目的

医学成像中的人工智能 (AI) 是一个新兴话题，涉及复杂图像结构的解释。深度学习技术的最新进展提高了无需人工干预即可提取重要特征的计算能力。内耳道 (IAC) 及其神经等细微组织的自动检测和分割是一项具有挑战性的任务，可以使用深度学习技术进行改进。

方法

这项研究的主要范围是提出一种自动方法来检测和分割 IAC 及其神经，如面神经、耳蜗神经、下前庭神经和上前庭神经。为了解决这个问题，我们提出了一种由 U-net 驱动的 Mask R-CNN 方法来检测和分割 IAC 及其神经。Mask R-CNN 及其 RESNET50 模型的骨干网络学习基于背景的定位策略来生成 IAC 的实际边界框。此外，U-net 通过学习 IAC 的特征来分割 IAC 及其神经的结构相关信息。

结果

所提出的方法在包括成人和儿童在内的 50 名不同患者的临床数据集上进行了实验。使用联合交集（IoU）评估使用 Mask R-CNN 对 IAC 的定位，使用 Dice 相似系数评估 IAC 及其神经的分割。

结论

定位结果显示RESNET50、RESNET101的平均IoU分别为0.79和0.74。IAC及其神经使用区域生长、PSO和U-net方法的Dice相似系数分别为92%、94%和96%。结果表明，所提出的方法在IAC及其神经的定位和分割方面表现更好。因此，由于 IAC 的定位和分割是准确的，AI 可以帮助放射科医生做出正确的决定。