Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality.

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.

Three-dimensional periodontal investigations using a prototype handheld ultrasound scanner with spatial positioning reading sensor.

AIM: To demonstrate the feasibility of the 3D ultrasound periodontal tissue reconstruction of the lateral area of a porcine mandible using standard 2D ultrasound equipment and spatial positioning reading sensors. MATERIAL AND METHOD: Periodontal 3D reconstructions were performed using a free-hand prototype based on a 2D US scanner and a spatial positioning reading sensor. For automated data processing, deep learning algorithms were implemented and trained using semi-automatically seg-mented images by highly specialized imaging professionals. RESULTS: US probe movement analysis showed that non-parallel 2D frames were acquired during the scanning procedure. Comparing 3 different 3D periodontal reconstructions of the same porcine mandible, the accuracy ranged between 0.179 mm and 0.235 mm. CONCLUSION: The present study demonstrated the diagnostic potential of 3D reconstruction using a free-hand 2D US scanner with spatial positioning readings. The use of auto-mated data processing with deep learning algorithms makes the process practical in the clinical environment for assessment of periodontal soft tissues.

Inversion behavior in core- and cladding-pumped Yb-doped fiber photodarkening measurements.

Yb-doped fibers are widely used in applications requiring high average output powers and high power pulse amplification. Photodarkening of the Yb-doped silicate glass core potentially limits the lifetime or efficiency of such fiber devices. In many studies of photodarkening, two principal methods of controllably inducing an inversion are used, namely, cladding pumping and core pumping of the sample. We present simulation results describing the key differences in the inversion profiles of samples of different physical parameters in these two cases, and we discuss the problems and possibilities that arise in benchmarking fibers of various physical parameters. Based on the simulation and experimental work, we propose guidelines for photodarkening benchmarking measurements and show examples of measurements made within and outside of the guidelines.

Photodarkening rate in Yb-doped silica fibers.

Yb-doped fibers are widely used in laser applications requiring high average output powers and high-peak-power pulse amplification. Photodarkening (PD) is recognized as one limiting factor in these fibers when pumped with high-intensity radiation. We describe an approach for performing quantitative PD studies of fibers, and we present measurements of the rate of PD in Yb-doped single-mode fibers with varying inversion levels. The method is applicable to large-mode-area fibers. We observed a seventh-order dependence of the PD rate on the excited-state Yb concentration for two different fibers; this result implies that PD of a Yb-doped fiber source fabricated using a particular fiber will be strongly dependent on the configuration of the device.