Accuracy Report on a Handheld 3D Ultrasound Scanner Prototype Based on a Standard Ultrasound Machine and a Spatial Pose Reading Sensor

Ultrasonic mapping of midpalatal suture - An ex-vivo study

OBJECTIVE

Rapid maxillary expansion is a common orthodontic procedure to correct maxillary constriction. Assessing the midpalatal suture (MPS) expansion plays a crucial role in treatment planning to determine its effectiveness. The objectives of this preliminary investigation are to demonstrate a proof of concept that the palatal bone underlying the rugae can be clearly imaged by ultrasound (US) and the reconstructed axial view of the US image accurately maps the MPS patency.

METHODS

An ex-vivo US scanning was conducted on the upper jawbones of two piglet's carcasses before and after the creation of bone defects, which simulated the suture opening. The planar images were processed to enhance bone intensity distribution before being orderly stacked to fuse into a volume. Graph-cut segmentation was applied to delineate the palatal bone to generate a bone volume. The accuracy of the reconstructed bone volume and the suture opening was validated by the micro-computed tomography (µCT) data used as the ground truth and compared with cone beam computed tomography (CBCT) data as the clinical standard. Also included in the comparison is the rugae thickness. Correlation and Bland-Altman plots were used to test the agreement between the two methods: US versus µCT/CBCT.

RESULTS

The reconstruction of the US palatal bone volumes was accurate based on surface topography comparison with a mean error of 0.19 mm for pre-defect and 0.15 mm and 0.09 mm for post-defect models of the two samples, respectively when compared with µCT volumes. A strong correlation (R2 ≥ 0.99) in measuring MPS expansion was found between US and µCT/CBCT with MADs of less than 0.05 mm, 0.11 mm and 0.23 mm for US, µCT and CBCT, respectively.

CONCLUSIONS

It was possible to axially image the MPS opening and rugae thickness accurately using high-frequency ultrasound.

CLINICAL SIGNIFICANCE

This study introduces an ionizing radiation-free, low-cost, and portable technique to accurately image a difficult part of oral cavity anatomy. The advantages of conceivable visualization could promise a successful clinical examination of MPS to support the predictable treatment outcome of maxillary transverse deficiency.

3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation-Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography

The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization of the scanned area: the carotid artery wall, the carotid artery circulated lumen, soft plaque, and calcified plaque. A qualitative evaluation was conducted via comparing the US reconstruction results with the CT angiographies of healthy and carotid-artery-disease patients. The overall scores for the automated segmentation using the MultiResUNet model for all segmented classes in our study were 0.80 for the IoU and 0.94 for the Dice. The present study demonstrated the potential of the MultiResUNet-based model for 2D-ultrasound-image automated segmentation for atherosclerosis diagnosis purposes. Using 3D ultrasound reconstructions may help operators achieve better spatial orientation and evaluation of segmentation results.

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.