Radu Chifor1,2, Mircea Hotoleanu3, Tiberiu Marita4, Tudor Arsenescu2, Mihai Adrian Socaciu5, Iulia Clara Badea1, Ioana Chifor1. 1. Department of Preventive Dentistry, University of Medicine and Pharmacy Iuliu Hatieganu, 400083 Cluj-Napoca, Romania. 2. Chifor Research SRL, 400068 Cluj-Napoca, Romania. 3. Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania. 4. Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania. 5. Department of Radiology and Imaging, University of Medicine and Pharmacy "Iuliu Hatieganu", 400162 Cluj-Napoca, Romania.
Abstract
This research aimed to evaluate Mask R-CNN and U-Net convolutional neural network models for pixel-level classification in order to perform the automatic segmentation of bi-dimensional images of US dental arches, identifying anatomical elements required for periodontal diagnosis. A secondary aim was to evaluate the efficiency of a correction method of the ground truth masks segmented by an operator, for improving the quality of the datasets used for training the neural network models, by 3D ultrasound reconstructions of the examined periodontal tissue. METHODS: Ultrasound periodontal investigations were performed for 52 teeth of 11 patients using a 3D ultrasound scanner prototype. The original ultrasound images were segmented by a low experienced operator using region growing-based segmentation algorithms. Three-dimensional ultrasound reconstructions were used for the quality check and correction of the segmentation. Mask R-CNN and U-NET were trained and used for prediction of periodontal tissue's elements identification. RESULTS: The average Intersection over Union ranged between 10% for the periodontal pocket and 75.6% for gingiva. Even though the original dataset contained 3417 images from 11 patients, and the corrected dataset only 2135 images from 5 patients, the prediction's accuracy is significantly better for the models trained with the corrected dataset. CONCLUSIONS: The proposed quality check and correction method by evaluating in the 3D space the operator's ground truth segmentation had a positive impact on the quality of the datasets demonstrated through higher IoU after retraining the models using the corrected dataset.
This research aimed to evaluate Mask R-CNN and U-Net convolutional neural network models for pixel-level classification in order to perform the automatic segmentation of bi-dimensional images of US dental arches, identifying anatomical elements required for periodontal diagnosis. A secondary aim was to evaluate the efficiency of a correction method of the ground truth masks segmented by an operator, for improving the quality of the datasets used for training the neural network models, by 3D ultrasound reconstructions of the examined periodontal tissue. METHODS: Ultrasound periodontal investigations were performed for 52 teeth of 11 patients using a 3D ultrasound scanner prototype. The original ultrasound images were segmented by a low experienced operator using region growing-based segmentation algorithms. Three-dimensional ultrasound reconstructions were used for the quality check and correction of the segmentation. Mask R-CNN and U-NET were trained and used for prediction of periodontal tissue's elements identification. RESULTS: The average Intersection over Union ranged between 10% for the periodontal pocket and 75.6% for gingiva. Even though the original dataset contained 3417 images from 11 patients, and the corrected dataset only 2135 images from 5 patients, the prediction's accuracy is significantly better for the models trained with the corrected dataset. CONCLUSIONS: The proposed quality check and correction method by evaluating in the 3D space the operator's ground truth segmentation had a positive impact on the quality of the datasets demonstrated through higher IoU after retraining the models using the corrected dataset.
Authors: S Kakizaki; A Aoki; M Tsubokawa; T Lin; K Mizutani; G Koshy; A Sadr; S Oda; Y Sumi; Y Izumi Journal: J Periodontal Res Date: 2017-10-24 Impact factor: 4.419
Authors: K C T Nguyen; D Q Duong; F T Almeida; P W Major; N R Kaipatur; T T Pham; E H M Lou; M Noga; K Punithakumar; L H Le Journal: J Dent Res Date: 2020-05-11 Impact factor: 6.116
Authors: Sigrun Eick; Jasmin Nydegger; Walter Bürgin; Giovanni E Salvi; Anton Sculean; Christoph Ramseier Journal: Clin Oral Investig Date: 2018-02-21 Impact factor: 3.573