Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

Featured Application Convolutional neural networks can accurately identify the Inferior Alveolar Canal, rapidly generating precise 3D data. The datasets and source code used in this paper are publicly available, allowing the reproducibility of the experiments performed. Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation

A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal

In recent years, deep learning has been employed in several medical fields, achieving impressive results. Unfortunately, these algorithms require a huge amount of annotated data to ensure the correct learning process. When dealing with medical imaging, collecting and annotating data can be cumbersome and expensive. This is mainly related to the nature of data, often three-dimensional, and to the need for well-trained expert technicians. In maxillofacial imagery, recent works have been focused on the detection of the Inferior Alveolar Nerve (IAN), since its position is of great relevance for avoiding severe injuries during surgery operations such as third molar extraction or implant installation. In this work, we introduce a novel tool for analyzing and labeling the alveolar nerve from Cone Beam Computed Tomography (CBCT) 3D volumes

Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe

Assessment of Anterior Loop of Inferior Alveolar Nerve at the Mental Foramen: A CBCT study

PURPOSE: Sufficient area in the interforaminal region is required for dental implant placement, anterior mandibular surgeries and the anterior loop of the IAN is located within the limits of this area. AIM OF THE STUDY: The purpose of this descriptive study was to assess the prevalence and extent of Anterior Loop of Inferior Alveolar Nerve at the Mental Foramen by using CBCT. OBJECTIVES: The objective of the study was to evaluate the prevalence & variations in length of anterior loop of the inferior alveolar nerve and to make clinical applications in anterior mandibular surgeries by using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: CBCT images from 127 patients (254 hemimandibles) obtained for various clinical indications were randomly selected and evaluated to determine the presence and length of the anterior loop. The length of the anterior loop was then compared based on gender, side and age of the mandible. The data were analyzed using the Pearson chi-square test and linear regression analysis. RESULTS: An anterior loop was identified in 55.9% of the cases, and its length ranged from 0.25 mm to 3.25 mm (mean - 1.00±0.79mm). The loop had a greater mean length and was significantly more prevalent in females (p=0.02). Significant differences were found among different age groups (p = <0.0001). Anterior loop was found commonly in the age group of 36-45, 46-55 (p = 0.02, p = 0.02). CONCLUSION: In this study, a high prevalence of the anterior loop of IAN was found, and its length varied greatly, in most cases it was less than 1 mm long. Although this is a prevalent anatomical variation, safety limits for the placement of implants in this region cannot be established before an accurate evaluation using imaging techniques in order to identify and preserve the neurovascular bundles

Comparitive Assessment of the Relationship between the Third Molar and the Inferior Alveolar Nerve using Panoramic Radiographs and 3-Dimensional Object Reconstructed from CT Data

Conventional Panoramic Radiograph is a projected view, only shows limited information whereas 3-Dimensional Object Reconstruction shows all the information regarding mandibular third molar apices to the Inferior Alveolar Nerve. Rotating the image can show its bucco-lingual relation, distance also can be measured between them. Simulation can be done by moving the tooth in its path of exit, can plan and view sectioning of tooth preoperatively. Definitely 3-Dimensional Object reconstruction is better than Conventional Panoramic radiograph as confirmed by the similarity between our data and those in the literature. As Conventional Panoramic radiograph is not showing adequate and necessary information, CT scan can be prescribed as a routine radiographic investigation and 3-Dimensional object reconstruction can be done from CT data and visualize actual anatomy present. But for clinicians and patients the only disadvantage of CT scan is its high radiation which can be overseen when compared to its advantages

Inferior Alveolar Nerve Segmentation in CBCT images using Connectivity-Based Selective Re-training

Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in many dental and maxillofacial surgery applications to prevent irreversible damage to the nerve during the procedure.The ToothFairy2023 Challenge aims to establish a 3D maxillofacial dataset consisting of all sparse labels and partial dense labels, and improve the ability of automatic IAN segmentation. In this work, in order to avoid the negative impact brought by sparse labeling, we transform the mixed supervised problem into a semi-supervised problem. Inspired by self-training via pseudo labeling, we propose a selective re-training framework based on IAN connectivity. Our method is quantitatively evaluated on the ToothFairy verification cases, achieving the dice similarity coefficient (DSC) of 0.7956, and 95\% hausdorff distance (HD95) of 4.4905, and wining the champion in the competition. Code is available at https://github.com/GaryNico517/SSL-IAN-Retraining.Comment: technical paper for Miccai ToothFairy2023 Challeng

Annotating the Inferior Alveolar Canal: the Ultimate Tool

The Inferior Alveolar Nerve (IAN) is of main interest in the maxillofacial field, as an accurate localization of such nerve reduces the risks of injury during surgical procedures. Although recent literature has focused on developing novel deep learning techniques to produce accurate segmentation masks of the canal containing the IAN, there are still strong limitations due to the scarce amount of publicly available 3D maxillofacial datasets. In this paper, we present an improved version of a previously released tool, IACAT (Inferior Alveolar Canal Annotation Tool), today used by medical experts to produce 3D ground truth annotation. In addition, we release a new dataset, ToothFairy, which is part of the homonymous MICCAI2023 challenge hosted by the Grand-Challenge platform, as an extension of the previously released Maxillo dataset, which was the only publicly available. With ToothFairy, the number of annotations has been increased as well as the quality of existing data