In International journal of computer assisted radiology and surgery
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.
Jeevakala S, Sreelakshmi C, Ram Keerthi, Rangasami Rajeswaram, Sivaprakasam Mohanasankar
Internal auditory canal, Localization, Mask R-CNN, Segmentation, U-net