Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images

Segmentation of head and neck cancer (HNC) primary tumors on medical images is an essential, yet labor-intensive, aspect of radiotherapy. PET/CT imaging offers a unique ability to capture metabolic and anatomic information, which is invaluable for tumor detection and border definition. An automatic segmentation tool that could leverage the dual streams of information from PET and CT imaging simultaneously, could substantially propel HNC radiotherapy workflows forward. Herein, we leverage a multi-institutional PET/CT dataset of 201 HNC patients, as part of the MICCAI segmentation challenge, to develop novel deep learning architectures for primary tumor auto-segmentation for HNC patients. We preprocess PET/CT images by normalizing intensities and applying data augmentation to mitigate overfitting. Both 2D and 3D convolutional neural networks based on the U-net architecture, which were optimized with a model loss function based on a combination of dice similarity coefficient (DSC) and binary cross entropy, were implemented. The median and mean DSC values comparing the predicted tumor segmentation with the ground truth achieved by the models through 5-fold cross validation are 0.79 and 0.69 for the 3D model, respectively, and 0.79 and 0.67 for the 2D model, respectively. These promising results show potential to provide an automatic, accurate, and efficient approach for primary tumor auto-segmentation to improve the clinical practice of HNC treatment

The solution of elasticity problems for the half-space by the method of Green and Collins

The paper reviews the method of complex potential functions developed by Green and Collins as applied to axisymmetric mixed boundary value problems in elasticity for the half-space. It is shown how the method can be applied to problems in several coupled potential functions such as adhesive and frictional contact problems, to problems involving annular regions and to problems in thermoelasticity. Attention is given to the question of choosing a formulation which leads to a well-behaved numerical solution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41802/1/10494_2004_Article_BF00386216.pd