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

doi:10.1101/2021.07.22.21260825

Cited by 7 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose 2 subjects representative of our test data set as well as the “outlier case” to illustrate our results. Figure 3 shows reference and predicted landmarks plotted on the fully automatically obtained CT scan segmentations (Dot et al 2022).…”

Section: Resultsmentioning

confidence: 99%

“…From a clinical viewpoint, additional verification and correction of the results could be performed on a visualization of the predicted landmarks plotted on 3D models obtained fully automatically via DL (Fig. 3) (Wang et al 2021; Dot et al 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Two hundred presurgical CT scans, randomly selected and anonymized, were obtained from a retrospective sample described in a previous study (Dot et al 2022), consisting of consecutive patients having undergone orthognathic surgery between January 2017 and December 2019 in a single maxillofacial surgery department. Patients referred to this university hospital located in a cosmopolitan European capital city were ethnically diverse and presented a variety of dentofacial deformities within the scope of orthognathic surgery (maxilla and/or mandible surgery, usually performed along with orthodontic treatment).…”

Section: Data Setmentioning

confidence: 99%

See 2 more Smart Citations

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Data Setmentioning

confidence: 99%

See 1 more Smart Citation

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…We chose two subjects representative of our test dataset as well as the "outlier case" to illustrate our results. Figure 3 shows reference and predicted landmarks plotted on the fully automatically-obtained CT scan segmentations (Dot et al 2022).…”

Section: Three-dimensional Visualizationmentioning

confidence: 99%

Automatic Three-Dimensional Cephalometric Landmarking via Deep Learning

Dot

Schouman

Chang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The increasing use of three-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 tedious manual landmarking. In this 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 in 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, one 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 ± 1.3mm, while success detection rates for 2.0, 2.5 and 3.0mm were 90.4%, 93.6% and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7mm 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.

show abstract

“…Several articles are present in the literature with specific deep learning models for automatic segmentation of the maxillofacial structures, mandibular canal, cephalometric landmarks, cervical vertebras, and maxillofacial defects such as cleft palate. The majority of these models had U-Net architecture with a high (90%-95%) Dice Similarity Coefficient 11 – 26 .…”

Section: Introductionmentioning

confidence: 99%

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients

Orhan

Shamshiev

Ezhov

et al. 2022

Sci Rep

View full text Add to dashboard Cite

This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.

show abstract

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

Cited by 7 publications

References 21 publications

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

Automatic Three-Dimensional Cephalometric Landmarking via Deep Learning

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients

Contact Info

Product

Resources

About