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

2022 ◽  
Author(s):  
Gauthier Dot ◽  
Thomas Schouman ◽  
Guillaume Dubois ◽  
Philippe Rouch ◽  
Laurent Gajny
Keyword(s):  
Orthognathic Surgery ◽  
Automatic Segmentation ◽  
Ct Scans ◽  
Computer Assisted ◽  
Surgery Planning ◽  
Fully Automatic ◽  
Net Framework

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

2021 ◽  
Author(s):  
Gauthier Dot ◽  
Thomas Schouman ◽  
Guillaume Dubois ◽  
Philippe Rouch ◽  
Laurent Gajny
Keyword(s):  
Orthognathic Surgery ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Mandibular Canal ◽  
Similarity Coefficient ◽  
Ct Scans ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Industry Expert ◽  
Fully Automatic

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-definition 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 segmentations of the mandible. Results In the test cohort, mean volumetric Dice Similarity Coefficient (vDSC) & surface Dice Similarity Coefficient at 1mm (sDSC) were 0.96 & 0.97 for the upper skull, 0.94 & 0.98 for the mandible, 0.95 & 0.99 for the upper teeth, 0.94 & 0.99 for the lower teeth and 0.82 & 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.

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans

2016 ◽  
Vol 38 (12) ◽  
pp. 1474-1480 ◽  
Author(s):  
Diogo F. Almeida ◽  
Rui B. Ruben ◽  
João Folgado ◽  
Paulo R. Fernandes ◽  
Emmanuel Audenaert ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Medullary Canal ◽  
Ct Scans ◽  
Low Resolution ◽  
Fully Automatic

Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks

2021 ◽  
Vol 159 (6) ◽  
pp. 824-835.e1
Author(s):  
Rosalia Leonardi ◽  
Antonino Lo Giudice ◽  
Marco Farronato ◽  
Vincenzo Ronsivalle ◽  
Silvia Allegrini ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Pharyngeal Airway ◽  
Fully Automatic

scholarly journals Automatic segmentation and classification of seven-segment display digits on auroral images

2016 ◽  
Vol 5 (2) ◽  
pp. 305-314 ◽  
Author(s):  
Tuomas Savolainen ◽  
Daniel Keith Whiter ◽  
Noora Partamies
Keyword(s):  
Automatic Segmentation ◽  
Scientific Information ◽  
Spherical Mirror ◽  
Automatic Method ◽  
Time Period ◽  
Film Images ◽  
Fully Automatic ◽  
Night Sky

Abstract. In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a dataset consisting of about 7 million auroral all-sky images taken during the time period of 1973–1997 at camera stations centred around Sodankylä observatory in northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information but are impractical to use without an automatic method for extracting the date–time from the display. We describe the implementation and the results of such a method in detail in this paper.

Sign in / Sign up

Export Citation Format

Share Document