Index

Augmentation of CT Images by Variation of Non-Rigid Deformation Vector Field Amplitudes

Synergistic Radiomics and CNN Features for Multiparametric MRI Lesion Classification

Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year. In order to assist in diagnosing patients with breast cancer, to measure the size of the existing breast tumors and to check for tumors in the opposite breast, breast magnetic resonance imaging (MRI) can be applied. MRI enjoys the advantages that patients won’t suffer from ionizing radiation during the examination, and it can capture the entire breast volume. In the meanwhile, machine learning methods have been proved to accurately classify images by assigning the probability score to estimate the likelihood of an image belonging to a certain category in many fields. With the properties mentioned above, this project aims to investigate whether applying machine learning approaches to breast tumor MRI can provide an accurate prediction on the tumor type (malignant or benign) for the diagnosing purpose.

Dilated deeply supervised networks for hippocampus segmentation in MR

Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer’s Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks. We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.

Analysis of NVIDIA Optix Engine for Ray Tracing in SPECT

Looking for a student for the project: Analysis of NVIDIA Optix as a Ray Tracing platform for SPECT forward projection

Topic motivation

Ray tracing is massively used in videogames to determine what object within the scene should be shown in the viewpoint of the observer.
Furthermore Ray Tracing is also used to determine the shadows, lights and reflections to be portrayed in the screen.
Optix is an extremely powerful API designed by NVIDIA due to its modularity and its flexibility. In 2015, Optix was used to model a SPECT system, achieving a significant speed up over other simulation frameworks for the same task [1]

Project description

The project would consist of five parts:

Part I: Set up Optix as a ray tracing framework for nuclear imaging, without physics
Part II: Run a simulation with a simple SPECT parallel hole collimator
Part III: Set up Optix as a ray tracing framework for nuclear imaging, with physics
Part IV: Validation of the tool with simulated data from SIMIND (data provided)
Part V: Validation of the tool with data acquired from a system (data provided)

Success measurements:

Project would be considered successful after step II
At step IV, it could become a conference paper.

Other information:

Topic can be 5 or 10 ECTS Research/Master Project. Can also be extended to a thesis.
Contact: maximilian.reymann@fau.de
Applicants ideally have experience with C++ or GPU programming, or are looking to gain expertise in these areas.

GAN Generated Model Observer for one Class Detection in SPECT Imaging

CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction

Development of a deep learning-based phoneme recognizer for noisy speech

For speech intelligibility, consonants have a fundamental importance. Thus, the assumption can be made that automatic phoneme and especially consonant recognition correlates well with human speech intelligibility.
In noisy environments however, speech and especially consonants may be degraded.

In this project, we want to study the effect of noise on speech intelligibility. Therefore, we train a neural network to recognize phoneme based on the TIMIT dataset. We will add diffent noise types and noise levels to the speech signal and study the effect on the recognition rate.

This project requires no preliminary knowledge in deep learning, although may be beneficial. Basic signal processing concepts like sampling theorem and FFT should be present.

Adapting Pyro-NN with SPECT operators

Goal of this project is the implementation of the SPECT Forward and backward projection model in the Pyro-NN Framework. This would enable to include the SPECT reconstruction process into a Neural Network architecture.

Advancing the digital twin method

The aim of this research project was to develop a program that registers an XCAT phantom to a CT scan with a rigid and a non-rigid registration. The registered XCAT Phantom can be used to perform SPECT experiments and simulations without burdening the patient with an additional SPECT examination. To perform the registration the open source software Plastimatch version 1.8.0 was used. The results of the registration were evaluated visually and empirically. The registration was successful in most of the cases, but there were some cases where the rigid registration direction failed.