Student Opportunities

We are constantly looking for students with an interest in medical image analysis, as well as the use of machine learning and computer vision in novel and established clinical and forensic applications. Please contact Darko Stern if you are interested in joining our group. This page lists specific open student projects and also gives an overview of currently running and already finished projects on a master and bachelor level. Students coming with their own research ideas are also welcome to get in contact!

Unless otherwise stated, projects are not financially supported. 

Open Student Projects

OPEN: Deep learning for archaeological interpretation of GPR data

Suitable as: Master's Thesis

Ground penetrating radar (GPR) allows to record 3D data of near-surface soil regions in a non-invasive way. In archaeology, it is an invaluable tool for the non-destructive exploration of archaeological monuments and other manmade structures buried in the ground. State-of-the art mobile multi-channel GPR recording setups enable archaeologists to record square kilometres at approx. 5 cm of spatial resolution, which leads to huge datasets.

The archaeological interpretation of such datasets is challenging for various reasons, like the limited spatial resolution and the susceptibility of the acquisitions process to environmental influences. Furthermore, structures of interests degrade over time due to, e.g. erosion and human intervention. Currently, the time-consuming interpretation of GPR data is performed by experts, which cannot keep pace with the increasing amount of data.

The aim of this project will be to investigate the applicability existing deep convolutional network (DCNN) architectures to GPR data by adapting and training them to detect the remains of man-made structures. Moreover, a software toolbox for efficient handling of practical datasets should be developed.

Note: Compensation is negotiable

Detailed Description

OPEN: Instance Segmentation in Medical Image Applications

Suitable as: Master's Project / Thesis

To start answering fundamental questions for understanding how the brain works, we need to look at the brain structure on the cell levels. Reconstruction of cell morphology and building connectivity diagram requires that all instances of neuron cell are segmented. Differently, to semantic segmentation, instance segmentation does not only assign a class label to each pixel of an image but also distinguishes between instances within each class, e.g., each individual cell in an electronic microscopy image gets assigned a unique ID [1]. This work will investigate interesting direction for simultaneous segmentation of all instances by automatically encoding the individual instances as pixel-wise embeddings.

[1] Payer et al., Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks, MICCAI, 2018

Detailed Description

OPEN: Bayesian Deep Learning in Medical Imaging

Suitable as: Master's Project / Thesis

The application of Bayesian theory to the deep learning framework recently has attracted the attention of both the computer vision and medical imaging community and is a currently growing field of research. By extending the mathematically grounded theory of neural networks with Bayesian theory, the ability to capture the uncertainty present in the data the model’s weights is gained. With this, not only comparable performance to current state-of-the-art results in applications like classification, segmentation, and regression, can be reached, but also the quality of the predictions can be assessed by their predictive uncertainty. The ability to reason about the data and model uncertainty [1,2] is of crucial importance for many applications that are related to decision making.

[1] Kendall et al., What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, NIPS, Long Beach, USA, 2017
[2] Blundell et al., Weight Uncertainty in Neural Networks, ICML 2015

Detailed Description

OPEN: Low-Dose CT Reconstruction Using Deep Learning

Suitable as: Master's Project / Thesis

Computed tomography (CT) is a widely used medical imaging modality to generate a volumetric image representing the interior structure of a subject. To reconstruct a three dimensional (3D) CT image, a series of two dimensional (2D) X-ray based projections are acquired from different views of the subject. While the Filtered Backprojection (FBP) method yields an analytical solution to reconstruct a 3D CT image from these 2D X-ray projections, it also relies on a large number of them which correlates to the amount of ionizing radiation the subject is exposed to. In order to decrease the amount of ionizing radiation and consequently the subject’s risk to develop cancer, new CT reconstruction approaches that yield a decent image quality even from a low radiation dose need to be investigated. Recent research in low-dose CT reconstruction employed deep convolutional neural networks (CNNs) to find low-dose solutions to this problem. The goal of this project is to explore and evaluate deep learning based low-dose CT reconstruction approaches to investigate new solutions to this demanding problem.

[1] Thaler et al., Sparse-View CT Reconstruction Using Wasserstein GANs, International Workshop on Machine Learning for Medical Image Reconstruction, 2018

Detailed Description

OPEN: Rotation Invariant Deep Neural Networks

Suitable as: Master's Project / Thesis

Deep convolutional neural networks (DCNN) have recently shown outstanding performance on image classification and object detection tasks due to their powerful multiscale filters. The dominant filters used in building DCNN architectures are only transitionally invariant, which is not optimal when the problem is rotation equivalent, as it is the case in e.g. cells detection and tracking task. Thus, by explicitly encoding the expected rotational invariance of the object in the image [1][2], the complexity of the problem is decreased, leading to a reduction in the size of the required model.

[1] Marcos et al., Rotation equivariant vector field networks, ICCV, Venice, Italy, 2017

[2] Bekkers et al., Roto-Translation Covariant Convolutional Networks for Medical Image Analysis, MICCAI, Granada, Spain, 2018.

Detailed Description

Currently Ongoing Student Projects

TAKEN: Semi-Supervised Multi-Label Whole Heart Segmentation

Suitable as: Master's Project / Thesis

Student currently working on this project: Elisabeth Rechberger

Deep learning boosted the state-of-the-art in many computer vision and medical imaging tasks. Unfortunately, deep convolutional neural networks (CNNs) require lots of data to be trained successfully and deliver good performance. Especially in the medical imaging domain, obtaining large annotated datasets is not only difficult due to financial and ethical reasons, but also due to demanding annotations from medical experts.

Semi-supervised learning is a way of alleviating the requirement of lots of annotations, by also using information from images, where no annotations are available [1]. The goal of this project is to apply semi-supervised learning techinques for semantic segmentation problems, specifically for multi-label whole heart segmentation [2]. For this project, the student has to do a literature overview of semi-supervised semantic segmentation using CNNs in the medical imaging domain and to implement and compare various semi-supervised learning methods, e.g., using generative adverserial networks (GANs) to regularize the predicted segmentations.

[1] Yang et al., Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018
[2] Payer et al., Multi-Label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations, International Workshop on Statistical Atlases and Computational Models of the Heart, 2017

Detailed Descrition

TAKEN: Detection of Infected Teeth in 3D CBCT Images

Suitable as: Bachelor's Thesis/Master's Project /Master's Thesis

Student currently working on this project: Arnela Hadzic

As a consequence of a bacterial infection, tooth associated infection is very common. Those pathologies are usually located in the surrounding of the root of the teeth. They can vary in diameter from a simple widening of the periodontal space up to several millimeters or more, being completely bone surrounded or perforating the adjacent anatomical borders. Furthermore, they potentially affect each of the around 30 roots per jaw. The manual location of those frequently requires a large amount of work, depending on the number of investigated teeth and the quality of the data set as well as on the education and experience of the doctor doing an examination. The aim of the project is to train deep convolutional neural networks (DCNN) to automatically recognize all the infected teeth in the 3D Cone Beam Computed Tomography (CBCT) image.

The project is in collaboration with Dr. Barbara Kirnbauer, LKH Graz, Department of Dentistry and Oral Health.

[1] Payer et al., Integrating spatial configuration into heatmap regression based CNNs for landmark localization, Medical Image Analysis, 2019

Detailed Description

TAKEN: Deep Active Learning for Semantic Segmentation

Suitable as: Master's Project / Thesis

Student currently working on this project: Johannes Franz Spöcklberger 

The exponential growth of data contributed significantly to the success of Deep Learning in the last decade. While more data often leads to a better performance, there are practical limitations to consider. First, it can be infeasible to acquire additional data in a significant quantity. Second, annotating data is a laborious process that can quickly become very cost-intensive, especially when human experts are required. Last, some data samples can be detrimental to the overall performance of the model and are preferably excluded from training.

Active Learning (AL) mitigates these shortcomings by focusing the annotation effort solely on the most informative samples in an iterative learning procedure. To accomplish this, an AL system proposes a subset of data samples in an unsupervised manner and requests annotations from a human expert. This subset is then added to the annotated data pool and used to train a model to solve the given task. When training finishes, the trained model is used to propose another subset of samples to be annotated and the procedure starts over until the predefined total annotation effort of the human expert is reached [1].

The goal of this project is to apply Deep AL to solve semantic segmentation problems on cell images using measures like dropout as a query function to propose data samples for annotation.

[1] Yang et al., Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017

Detailed Description

TAKEN: Deep Reinforcement Learning in Medical Image Applications

Suitable as: Master's Project / Thesis

Student currently working on this project: Klemens Kasseroller

By learning a sequence of actions that maximize the expected reward, deep reinforcement learning (DRL) brought significant performance improvements in many areas including games, robotics, natural language processing, and computer vision. It was DeepMind, a small and little-known company in 2013, that achieved a breakthrough in the world of reinforcement learning as they implemented a system that could learn to play many classic Atari games with human or even superhuman performance. Sill, it was until recently that DRL started to appear also in medical image applications for landmark detection, automatic view planning from 3D MR images, or active breast lesion detection [1,2].

[1] Ghesu et al., Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

[2] Alansary et al., Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents, MICCAI, Granada, Spain, 2018

Detailed Description

Finished Student Projects

TAKEN: Bayesian Neural Networks in Medical Image Applications

Suitable as: Master's Thesis

Student currently working on this project: Stefan Eggenreich 

Project is combined with: Age estimation and growth prediction from MRI data of the knee

While standard deep convolution neural networks have recently shown unprecedented results that even go beyond human performance in computer vision tasks like classification, segmentation or detection, these methods are not capable of capturing model uncertainty. Being able to provide a prediction together with its uncertainty is of crucial importance for many medical applications that are related to decision making. Bayesian probability theory offers us mathematically grounded tools to reason about model uncertainty [1][2]. Therefore, Bayesian deep learning, as a field at the intersection between deep learning and Bayesian probability theory, has recently attracted great interest of both computer vision and medical image communities.

[1] Blundell et al., Weight Uncertainty in Neural Networks, ICML 2015

[2] Kendall et al., What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, NIPS, Long Beach, USA, 2017

Detailed Description

FINISHED (2018/03): Data Augmentation in Deep Learning using Generative Adversarial Networks

Student who worked on the topic: Thomas Neff

Link to Thomas' master's thesis.

Aim: Evaluate different strategies for synthetically augmenting image datasets for deep learning using recent generative models, especially generative adversarial networks.

The work in this student project has led to a publication at OAGM 2017, which received the best paper award, as well as a publication at OAGM 2018.

FINISHED (2017/12): Lung Lobe Segmentation from Thorax CT

Student who worked on the topic: Nicola Giuliani

Links to Nicola's master's thesis and VISAPP 2018 publication.

Aim: Investigate a lung lobe segmentation strategy for dual energy CT images provided by the Ludwig Boltzmann Institute for Lung Vascular Research (LBI-LVR).

Over the last years we have developed a GPU based vascular tree extraction scheme, which can be used for segmenting the pulmonary vessel tree from dual energy thorax CT data. The LBI-LVR is interested in determining early signs of pulmonary hypertension by performing a CT scan of the lung vessels and analyzing the vessel structures of disease and healthy patients.

When analyzing measures that try to predict pulmonary hypertension (PH) from the vascular tree, an interesting question is if a global measure for the whole tree is sufficient, or if individual measures of the different lung lobes give a more sensitive and specific prediction result. Therefore, in this master project we aim at developing a lung lobe segmentation algorithm, based on [1], to apply the quantitative measures predicting PH on the individual lobes. Students interested in this project should have C++ programming skills. Our libraries make extensive use of CUDA based image processing, therefore interest or even knowledge in this direction would be beneficial.

[1] Lassen, B.; van Rikxoort, E.M. ; Schmidt, M. ; Kerkstra, S. ; van Ginneken, B. ; Kuhnigk, J.-M. Automatic Segmentation of the Pulmonary Lobes From Chest CT Scans Based on Fissures, Vessels, and Bronchi. IEEE TMI 2013