Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands.

Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequent activation of the prosthetic digits accordingly. Vision-based grasp classification techniques offer improved coordination between amputees and prosthetic hands without physical contact. Deep learning methods, particularly Convolutional Neural Networks (CNNs), are utilized to process visual information for classification. The key challenge lies in developing a model that can effectively generalize across various object shapes and accurately classify grasp classes. To address this, a compact CNN model named GraspCNet is proposed, specifically designed for grasp classification in prosthetic hands. The use of separable convolutions reduces the computational burden, making it potentially suitable for real-time applications on embedded systems. The GraspCNet model is designed to learn and generalize from object shapes, allowing it to effectively classify unseen objects beyond those included in the training dataset. The proposed model was trained and tested using various standard object data sets. A cross-validation strategy has been adopted to perform better in seen and unseen object class scenarios. The average accuracy achieved was 82.22% and 75.48% in the case of seen, and unseen object classes respectively. In computer-based real-time experiments, the GraspCNet model achieved an accuracy of 69%. A comparative analysis with state-of-the-art techniques revealed that the proposed GraspCNet model outperformed most benchmark techniques and demonstrated comparable performance with the DcnnGrasp method. The compact nature of the GraspCNet model suggests its potential for integration with other sensing modalities in prosthetic hands.

Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull.

To propose an algorithm for automatic localization of 3D cephalometric landmarks on CBCT data, those are useful for both cephalometric and upper airway volumetric analysis. 20 landmarks were targeted for automatic detection, of which 12 landmarks exist on the mid-sagittal plane. Automatic detection of mid-sagittal plane from the volume is a challenging task. Mid-sagittal plane is detected by extraction of statistical parameters of the symmetrical features of the skull. The mid-sagittal plane is partitioned into four quadrants based on the boundary definitions extracted from the human anatomy. Template matching algorithm is applied on the mid-sagittal plane to identify the region of interest ROI, further the edge features are extracted, to form contours in the individual regions. The landmarks are automatically localized by using the extracted knowledge of anatomical definitions of the landmarks. The overall mean error for detection of 20 landmarks was 1.88 mm with a standard deviation of 1.10 mm. The cephalometric land marks on CBCT data were detected automatically with in the mean error less than 2 mm.

The reliability of different methods of manual volumetric segmentation of pharyngeal and sinonasal subregions.

OBJECTIVES: The purpose of the study was to test the intra and interobserver reliability of manual volumetric segmentation of pharyngeal and sinonasal airway subregions. STUDY DESIGN: Cone beam computed tomography data of 15 patients were collected from an orthodontic clinical database. Two experienced orthodontists independently performed manual segmentation of the airway subregions. Four performance measures were considered to test intra and interobserver reliability of manual segmentation: (1) volume correlation, (2) mean slice correlation, (3) percentage of volume difference, and (4) percentage of nonoverlapping voxels. RESULTS: Intra and interobserver reliability was observed to be greater than 0.96 for the entire pharyngeal and sinonasal airway sinus subregions by both observers using the volume correlation method. Mean slice correlation was found to be greater than 0.84, showing the existence of nonoverlapping voxels. Therefore, the percentage of nonoverlapping voxels was used as a reliability measure and was found to be less than 20% for both intra and interobserver markings. CONCLUSIONS: The mean slice correlation and percentage of nonoverlapping voxels were the most reliable performance measures of segmentation correctness. Volume correlation and the percentage of volume difference were observed to be the most reliable performance measures for volume correctness.

Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: A systematic review and meta-analysis of cephalometric studies.

Obstructive sleep apnea (OSA) is one of the common sleep breathing disorders in adults, characterised by frequent episodes of upper airway collapse during sleep. Craniofacial disharmony is an important risk factor for OSA. Overnight polysomnography (PSG) study is considered to be the most reliable confirmatory investigation for OSA diagnosis, whereas the precise localization of site of obstruction to the airflow cannot be detected. Identifying the cause of OSA in a particular ethnic population/individual subject helps to understand the etiological factors and effective management of OSA. The objective of the meta-analysis is to elucidate altered craniofacial anatomy on lateral cephalograms in adult subjects with established OSA. Significant weighted mean difference with insignificant heterogeneity was found for the following parameters: anterior lower facial height (ALFH: 2.48 mm), position of hyoid bone (Go-H: 5.45 mm, S-H: 6.89 mm, GoGn-H: 11.84°, GoGn-H: 7.22 mm, N-S-H: 2.14°), and pharyngeal airway space (PNS-Phw: -1.55 mm, pharyngeal space: -495.74 mm2 and oro-pharyngeal area: -151.15 mm2). Significant weighted mean difference with significant heterogeneity was found for the following parameters: cranial base (SN: -2.25 mm, S-N-Ba: -1.45°), position and length of mandible (SNB: -1.49° and Go-Me: -5.66 mm) respectively, maxillary length (ANS-PNS: -1.76 mm), tongue area (T: 366.51 mm2), soft palate area (UV: 125.02 mm2), and upper airway length (UAL: 5.39 mm). This meta-analysis supports the relationship between craniofacial disharmony and obstructive sleep apnea. There is a strong evidence for reduced pharyngeal airway space, inferiorly placed hyoid bone and increased anterior facial heights in adult OSA patients compared to control subjects. The cephalometric analysis provides insight into anatomical basis of the etiology of OSA that can influence making a choice of appropriate therapy.

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization.

PURPOSE: The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets. METHODS: A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization. RESULTS: Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions. CONCLUSION: The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.