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DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation

Article dans une revue avec comité de lecture
Author
DOT, Gauthier
353778 CHU Pitié-Salpêtrière [AP-HP]
557826 Université Paris Cité [UPCité]
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]
CHAURASIA, Akhilanand
DUBOIS, Guillaume
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]
SAVOLDELLI, Charles
414617 Institut Universitaire de la Face et du Cou [Nice]
HAGHIGHAT, Sara
351566 Shiraz University of Medical Sciences [Iran] [SUMS]
AZIMIAN, Sarina
223521 Iran University of Science and Technology [Tehran] [IUST]
ccRAHBAR TARAMSARI, Ali
SIVARAMAKRISHNAN, Gowri
ISSA, Julien
566614 Poznan University of Medical Sciences [Poland] [PUMS]
DUBEY, Abhishek
SCHOUMAN, Thomas
353778 CHU Pitié-Salpêtrière [AP-HP]
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]
ccGAJNY, Laurent
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]

URI
http://hdl.handle.net/10985/26183
DOI
10.1016/j.jdent.2024.105130
Date
2024-06
Journal
Journal of Dentistry

Abstract

Objectives: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomic structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. Methods: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. Results: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3% and a normalised surface distance (NSD) of 98.2 ± 2.2%. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4% and a NSD of 98.4 ± 3.6%. Conclusions: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. Clinical Significance: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

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