Automatic segmentation of mandibular canal using transformer based neural networks

Abstract

Accurate 3D localization of the mandibular canal is crucial for the success of digitally-assisted dental surgeries. Damage to the mandibular canal may result in severe consequences for the patient, including acute pain, numbness, or even facial paralysis. As such, the development of a fast, stable, and highly precise method for mandibular canal segmentation is paramount for enhancing the success rate of dental surgical procedures. Nonetheless, the task of mandibular canal segmentation is fraught with challenges, including a severe imbalance between positive and negative samples and indistinct boundaries, which often compromise the completeness of existing segmentation methods. To surmount these challenges, we propose an innovative, fully automated segmentation approach for the mandibular canal. Our methodology employs a Transformer architecture in conjunction with cl-Dice loss to ensure that the model concentrates on the connectivity of the mandibular canal. Additionally, we introduce a pixel-level feature fusion technique to bolster the model’s sensitivity to fine-grained details of the canal structure. To tackle the issue of sample imbalance and vague boundaries, we implement a strategy founded on mandibular foramen localization to isolate the maximally connected domain of the mandibular canal. Furthermore, a contrast enhancement technique is employed for pre-processing the raw data. We also adopt a Deep Label Fusion strategy for pre-training on synthetic datasets, which substantially elevates the model’s performance. Empirical evaluations on a publicly accessible mandibular canal dataset reveal superior performance metrics: a Dice score of 0.844, click score of 0.961, IoU of 0.731, and HD95 of 2.947 mm. These results not only validate the efficacy of our approach but also establish its state-of-the-art performance on the public mandibular canal dataset.

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

“…Nevertheless, being a 2D image, it cannot convey information about interior bone architecture [39]. CT is an advanced 3D medical imaging method that generates 3D digital dental radiographs of teeth, alveolar bone, oral soft tissue, and neurovascular systems [34]. CT utilizes advanced technology to provide detailed anatomical information and spatial correlations.…”

“…Although the U-Net architecture is widely used for MC segmentation, some recent studies have opted for different methods. These include YOLOv4 [48], Mask R-CNN [58], and transformer-based models [34]. Each of these brings unique strengths to the task, offering diverse ways to handle the complexities of dental imaging.…”

“…Lv et al [34] 3D CBCT This research utilizes a Transformer design combined with cl-Dice loss to highlight the connection of the MC. An approach is shown to improve the ability to detect small details in the canal structure by combining features at the pixel level.…”

“…In the context of MC segmentation, a method based on transformers addresses specific local details and overall semantic information of the MC [34]. This leads to consistent accuracy throughout the entire CBCT image.…”

“…Recent research commonly employs the U-Net and its modified architectures for MC segmentation. However, some studies also use YOLO to detect the MC location and transformer-based neural networks for subsequent MC segmentation [34]. In this context, it is evident that deep learning can be effectively employed for MC segmentation.…”