SAM

Accuracy and reliability of automatic three-dimensional cephalometric landmarking

DOT, Gauthier — Wed, 01 Jan 2020 00:00:00 GMT

Accuracy and reliability of automatic three-dimensional cephalometric landmarking DOT, Gauthier; RAFFLENBEUL, Frédéric; ARBOTTO, M.; SCHOUMAN, Thomas; ROUCH, Philippe; GAJNY, Laurent The aim of this systematic review was to assess the accuracy and reliability of automatic landmarking for cephalometric analysis of three-dimensional craniofacial images. We searched for studies that reported results of automatic landmarking and/or measurements of human head computed tomography or cone beam computed tomography scans in MEDLINE, Embase and Web of Science until March 2019. Two authors independently screened articles for eligibility. Risk of bias and applicability concerns for each included study were assessed using the QUADAS-2 tool. Eleven studies with test dataset sample sizes ranging from 18 to 77 images were included. They used knowledge-, atlas- or learning-based algorithms to landmark two to 33 points of cephalometric interest. Ten studies measured mean localization errors between manually and automatically detected landmarks. Depending on the studies and the landmarks, mean errors ranged from <0.50 mm to>5 mm. The two best-performing algorithms used a deep learning method and reported mean errors <2 mm for every landmark, approximating results of operator variability in manual landmarking. Risk of bias regarding patient selection and implementation of the reference standard were found, therefore the studies might have yielded overoptimistic results. The robustness of these algorithms needs to be more thoroughly tested in challenging clinical settings. PROSPERO registration number: CRD42019119637.

Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework

DOT, Gauthier — Sat, 01 Jan 2022 00:00:00 GMT

Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework DOT, Gauthier; SCHOUMAN, Thomas; DUBOIS, Guillaume; ROUCH, Philippe; GAJNY, Laurent Objectives To evaluate the performance of the nnU-Net open-source deep learning framework for automatic multi-task segmentation of craniomaxillofacial (CMF) structures in CT scans obtained for computer-assisted orthognathic surgery. Methods Four hundred and fifty-three consecutive patients having undergone high-resolution CT scans before orthognathic surgery were randomly distributed among a training/validation cohort (n = 300) and a testing cohort (n = 153). The ground truth segmentations were generated by 2 operators following an industry-certified procedure for use in computer-assisted surgical planning and personalized implant manufacturing. Model performance was assessed by comparing model predictions with ground truth segmentations. Examination of 45 CT scans by an industry expert provided additional evaluation. The model’s generalizability was tested on a publicly available dataset of 10 CT scans with ground truth segmentation of the mandible. Results In the test cohort, mean volumetric Dice similarity coefficient (vDSC) and surface Dice similarity coefficient at 1 mm (sDSC) were 0.96 and 0.97 for the upper skull, 0.94 and 0.98 for the mandible, 0.95 and 0.99 for the upper teeth, 0.94 and 0.99 for the lower teeth, and 0.82 and 0.98 for the mandibular canal. Industry expert segmentation approval rates were 93% for the mandible, 89% for the mandibular canal, 82% for the upper skull, 69% for the upper teeth, and 58% for the lower teeth. Conclusion While additional efforts are required for the segmentation of dental apices, our results demonstrated the model’s reliability in terms of fully automatic segmentation of preoperative orthognathic CT scans.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

DOT, Gauthier — Thu, 18 Aug 2022 00:00:00 GMT

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning DOT, Gauthier; SCHOUMAN, Thomas; CHANG, Shaole; RAFFLENBEUL, Frédéric; KERBRAT, Adeline; ROUCH, Philippe; GAJNY, Laurent The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set ( n = 160) and a test set ( n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator( n = 178) or twice by 3 operators ( n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

Three-Dimensional Cephalometric Landmarking and Frankfort Horizontal Plane Construction: Reproducibility of Conventional and Novel Landmarks

DOT, Gauthier — Fri, 01 Jan 2021 00:00:00 GMT

Three-Dimensional Cephalometric Landmarking and Frankfort Horizontal Plane Construction: Reproducibility of Conventional and Novel Landmarks DOT, Gauthier; RAFFLENBEUL, Frédéric; KERBRAT, Adeline; SCHOUMAN, Thomas; ROUCH, Philippe; GAJNY, Laurent In some dentofacial deformity patients, especially patients undergoing surgical orthodontic treatments, Computed Tomography (CT) scans are useful to assess complex asymmetry or to plan orthognathic surgery. This assessment would be made easier for orthodontists and surgeons with a three-dimensional (3D) cephalometric analysis, which would require the localization of landmarks and the construction of reference planes. The objectives of this study were to assess manual landmarking repeatability and reproducibility (R&R) of a set of 3D landmarks and to evaluate R&R of vertical cephalometric measurements using two Frankfort Horizontal (FH) planes as references for horizontal 3D imaging reorientation. Thirty-three landmarks, divided into “conventional”, “foraminal” and “dental”, were manually located twice by three experienced operators on 20 randomly-selected CT scans of orthognathic surgery patients. R&R confidence intervals (CI) of each landmark in the -x, -y and -z directions were computed according to the ISO 5725 standard. These landmarks were then used to construct 2 FH planes: a conventional FH plane (orbitale left, porion right and left) and a newly proposed FH plane (midinternal acoustic foramen, orbitale right and left). R&R of vertical cephalometric measurements were computed using these 2 FH planes as horizontal references for CT reorientation. Landmarks showing a 95% CI of repeatability and/or reproducibility > 2 mm were found exclusively in the “conventional” landmarks group. Vertical measurements showed excellent R&R (95% CI < 1 mm) with either FH plane as horizontal reference. However, the 2 FH planes were not found to be parallel (absolute angular difference of 2.41°, SD 1.27°). Overall, “dental” and “foraminal” landmarks were more reliable than the “conventional” landmarks. Despite the poor reliability of the landmarks orbitale and porion, the construction of the conventional FH plane provided a reliable horizontal reference for 3D craniofacial CT scan reorientation.

Biplanar Low-Dose Radiograph Is Suitable for Cephalometric Analysis in Patients Requiring 3D Evaluation of the Whole Skeleton

KERBRAT, Adeline — Fri, 01 Jan 2021 00:00:00 GMT

Biplanar Low-Dose Radiograph Is Suitable for Cephalometric Analysis in Patients Requiring 3D Evaluation of the Whole Skeleton KERBRAT, Adeline; RIVALS, Isabelle; DUPUY, Pauline; DOT, Gauthier; BERG, Britt-Isabelle; ATTALI, Valérie; SCHOUMAN, Thomas Background: The biplanar 2D/3D X-ray technology (BPXR) is a 2D/3D imaging system allowing simultaneous stereo-corresponding posteroanterior (PA) and lateral 2D views of the whole body. The aim of our study was to assess the feasibility of cephalometric analysis based on the BPXR lateral skull view to accurately characterize facial morphology. Method: A total of 17 landmarks and 11 angles were placed and/or calculated on lateral BPXR and lateral cephalograms of 13 patients by three investigators. Five methods of angle identification were performed: the direct construction of straight lines on lateral cephalograms (LC-A) and on BPXR (BPXR-A), as well as the calculation of angles based on landmark identification on lateral cephalograms (LA-L) and on BPXR with the PA image (BPXR-LPA) or without (BPXR-L). Intra- and interoperator reliability of landmark identification and angle measurement of each method were calculated. To determine the most reliable method among the BPXR-based methods, their concordance with the reference method, LC-A, was evaluated. Results: Both imaging techniques had excellent intra- and interoperator reliability for landmark identification. On lateral BPXR, BPXR-A presented the best concordance with the reference method and a good intra- and interoperator reliability. Conclusion: BPXR provides a lateral view of the skull suitable for cephalometric analysis with good reliability.

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

DOT, Gauthier — Sat, 01 Jun 2024 00:00:00 GMT

DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation DOT, Gauthier; CHAURASIA, Akhilanand; DUBOIS, Guillaume; SAVOLDELLI, Charles; HAGHIGHAT, Sara; AZIMIAN, Sarina; RAHBAR TARAMSARI, Ali; SIVARAMAKRISHNAN, Gowri; ISSA, Julien; DUBEY, Abhishek; SCHOUMAN, Thomas; GAJNY, Laurent 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.

Les enjeux de l’intelligence artificielle en odontologie

DOT, Gauthier — Thu, 01 Feb 2024 00:00:00 GMT

Les enjeux de l’intelligence artificielle en odontologie DOT, Gauthier; GAJNY, Laurent; DUCRET, Maxime Les applications potentielles de l’intelligence artificielle, ces algorithmes visant à améliorer l’efficacité et la sécurité de diverses décisions cliniques, sont nombreuses en odontologie. Alors que les premiers logiciels commerciaux commencent à être proposés, la plupart des algorithmes n’ont pas été solidement validés pour une utilisation clinique. Cet article décrit les enjeux entourant le développement de ces nouveaux outils, afin d’aider les praticiens à garder un regard éclairé et critique sur cette nouvelle approche.