Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article.

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes / Cipriano, Marco; Allegretti, Stefano; Bolelli, Federico; Di Bartolomeo, Mattia; Pollastri, Federico; Pellacani, Arrigo; Minafra, Paolo; Anesi, Alexandre; Grana, Costantino. - In: IEEE ACCESS. - ISSN 2169-3536. - 10:(2022), pp. 11500-11510. [10.1109/ACCESS.2022.3144840]

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes

Cipriano, Marco;Allegretti, Stefano;Bolelli, Federico
;
Pollastri, Federico;Anesi, Alexandre;Grana, Costantino
2022

Abstract

Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article.
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Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes / Cipriano, Marco; Allegretti, Stefano; Bolelli, Federico; Di Bartolomeo, Mattia; Pollastri, Federico; Pellacani, Arrigo; Minafra, Paolo; Anesi, Alexandre; Grana, Costantino. - In: IEEE ACCESS. - ISSN 2169-3536. - 10:(2022), pp. 11500-11510. [10.1109/ACCESS.2022.3144840]
Cipriano, Marco; Allegretti, Stefano; Bolelli, Federico; Di Bartolomeo, Mattia; Pollastri, Federico; Pellacani, Arrigo; Minafra, Paolo; Anesi, Alexandre; Grana, Costantino
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1258551
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