Index

Generative Adversarial Networks for Speech Vocoding

Semi-supervised Feature Learning for Orca Audio Signals using a Convolutional Autoencoder

Classification of Rotator Cuff Tears in MRI using Neural Networks

Automatic solar panel recognition, fault detection and localization in thermal images

Synthetic generation of CT image from non-attenuation corrected FDG-PET image using GANs and its application to whole-body PET/CT registration.

The primary aim of this research is to implement a Generative Adversarial Network (GAN) to synthesize CT images from non-attenuation corrected (NAC) FDG-PET images. Registration of multi-modality images (NAC-PET to CT) is a challenging problem due to variability of tissue or organ appearance. Hence, in order to reduce the variability, this work will investigate the use of GAN generated synthetic CT images to perform PET/CT registration.

Convolutional Neural Networks for multi-organ segmentation of SPECT projections

In this work we investigate the usage of deep learning techniques on SPECT data solving a multi-organ segmentation problem. We extract projections from 21 Lu-177 MELP SPECT scans and obtain the corresponding ground truth labels from the accompanied CT scans by forward-projection of 3D CT organ segmentations. We train a U-Net to predict the area of the kidney, spleen, liver, and background seen in the projection data, using a weighted dice loss between prediction and target labels to account for class imbalance.
With our method we achieved a mean dice coefficient of 72 % on the test set, encouraging us to perform further experiments using the U-Net.