March 19, 2012

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Dynamic analysis of a simplified flexible manipulator with interval joint clearances and random material properties

Abstract(#br)Flexible manipulator is an emerging technique in aerospace engineering, especially in the assembly, testing and maintenance of space stations. Dynamic analysis of a flexible manipulator with multiple clearance joints and hybrid uncertainties is a great challenge as compared to traditional flexible manipulator. To solve the problem, a dynamics model for a simplified flexible manipulator with interval clearance joints and random material properties was established. In this model, the Lankarani–Nikravesh contact force model was used to construct the clearance joint, while a combined feedforward–feedback control strategy based on a PID controller was applied to control the flexible manipulator. In addition, the clearance sizes and the Young’s moduli of the flexible parts were..

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging:A Transfer Learning Approach

Delineation of Gross Tumor Volume (GTV) is essential for the treatment of cancer with radiotherapy. GTV contouring is a time-consuming specialized manual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph nodes (GTVn) in oropharyngeal cancer patients using DL. The organizers of the HECKTOR 2022 challenge provided 3D Computed Tomography (CT) and Positron Emission Tomography (PET) scans with ground-truth GTV segmentations acquired from nine different centers. Bounding box cropping was applied to obtain an anatomic based region of interest. We used the Swin UNETR model in combination with transfer learning. The Swin UNETR encoder weights were initialized by pre-trained weights of a self-supervised Swin UNETR model. An average Dice score of 0.656 was achieved on a test set of 359 patients from the HECKTOR 2022 challenge. Code is available at: https://github.com/HC94/swin_unetr_hecktor_2022.</p

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.</p