ThesesPage

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Diploma, Master and Bachelor Theses

Running Theses

Photorealistic Rendering of Training Data for Object Detection and Pose Estimation with a Physics Engine (DA/MA/BA)

3D Object Detection is essential for many tasks such a Robotic Manipulation or Augmented Reality. Nevertheless, recording appropriate real training data is difficult and time consuming. Due to this, many approaches rely on using synthetic data to train a Convolutional Neural Network. However, those approaches often suffer from overfitting to the synthetic world and do not generalize well to unseen real scenes. There are many works that try to address this problem. In this work we try to follow , and intend to render photorealistic scenes in order to cope with this domain gap. Therefore, we will use a physics engine to generate physically plausible poses and use ray-tracing to render high-quality scenes. In this particular work, we will extend another thesis to improve the renderings' quality as e.g. enhance the rendering realism in terms of lightning and reflection.
Supervisor:Fabian Manhardt, Johanna Wald
Director:Federico Tombari
Student:
start-end:21.02.2020 -
Deep Learning for Tool Detection and Tracking in Microsurgery (Bachelor Thesis)

The aim of this project is the investigation of the state-of-the-art deep learning architectures and frameworks with the purpose of detection and tracking of instruments in retinal microsurgeries. An implementation of a deep learning based instrument detection workflow shall be provided at the end of the project.
Supervisor:Hasan Sarhan, Dr. Mehmet Yigitsoy
Director:Prof. Nassir Navab
Student:Luca Alessandro Dombetzki
start-end:01.04.2018 -
Uncertainty Aware Methods for Camera Pose Estimation in Images and 3-Dimensional Data (Project)

Camera pose estimation is the term for determining the 6-DoF rotation and translation parameters of a camera. It is now a key technology in enabling multitudes of applications such as augmented reality, autonomous driving, human computer interaction and robot guidance. For decades, vision scholars have worked on finding the unique solution of this problem. Yet, this trend is witnessing a fundamental change. The recent school of thought has begun to admit that for our highly complex and ambiguous real environments, obtaining a single solution is not sufficient. This has led to a paradigm shift towards estimating rather a range of solutions in the form of full probability or at least explaining the uncertainty of camera pose estimates. Thanks to the advances in Artificial Intelligence, this important problem can now be tackled via machine learning algorithms that can discover rich and powerful representations for the data at hand. In collaboration, TU Munich and Stanford University plan to devise and implement generative methods that can explain uncertainty and ambiguity in pose predictions. In particular, our aim is to bridge the gap between 6DoF pose estimation either from 2D images/3D point sets and uncertainty quantification through multimodal variational deep methods.
Supervisor:Dr. Shadi Albarqouni, Dr. Tolga Birdal
Director:Prof. Dr. Nassir Navab, Prof. Dr. Leonidas Guibas
Student:Mai Bui, Haowen Deng
start-end:01.01.2020 -
3D Pedestrian Detection and Pose Estimation (Hiwi)

Autonomous driving systems are right on the corner and one key concern around the development and social acceptance of such systems is safeguarding. In this project, we want to look at the task of pedestrian detection from LiDAR? point clouds and their 3D pose estimation from the RGB camera input. 3D object detection from sparse point cloud data and multiple pedestrian 3D pose estimation are two challenging tasks and therefore active research fields in both academia and industry. In this project, we want to integrate the state of the art deep learning methods, train models on synthetic renderings and improve their performance based on safeguarding KPIs defined.
Supervisor:Mahdi Saleh
Director:Federico Tombari
Student:
start-end: -
Evaluation of real-time dense reconstruction for robotic navigation (Master Thesis)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
Distributed SLAM - Jointly mapping 3D Geometry (DA/MA/BA)

Exploring an unknown scene and self-positioning within it, is a common and well-studied problem in Computer Vision which is known as SLAM (Simultaneous Localization and Mapping). Core fields of application are autonomous cooperative robotics and vehicles as well as tracking and detection systems in the medical domain. Traditional methods target a single system, equipped with image sensors, exploring the scene and building up a map for localization (e.g. a single robot or drone moving within an unknown environment). New approaches also incorporate information from other sensors such as IMUs, gyro or GPS. Another objective for the determination of the position is outside-in-tracking of an object via marker tracking with external sensors, thus providing the relative position of an object with respect to the tracking system. To overcome the line-of-sight problem of outside-in-tracking, and the singularity constraint of traditional SLAM methods, the project aims to develop a distributed SLAM approach. Multiple systems (referred to as sensor nodes hereafter), equipped with an image sensor, contribute to a common map of the scene for localization, while being also tracked by outside-in-tracking for accuracy. Thus, accuracy and applicability can be elevated with a distributed SLAM approach, combining the information of multiple sensor nodes and an external tracking system. Furthermore, the necessity of complicated and error prone calibration processes for individual systems within one application scenario can be avoided. The objective is to develop a generative distributed SLAM approach for challenging scenes and applications. Features like loop detection and closing, pose graph optimization, re-localization and mapping should be extended to a distributed approach, also enabling scalability.
Supervisor:Patrick Ruhkamp, Benjamin Busam
Director:Prof. Dr. Nassir Navab
Student:Joe Bedard
start-end: -
Dynamic objects in dense reconstruction (DA/MA/BA)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
3D GAN for conditional medical image synthesis and cross-modality translation (Master Thesis)

GAN in 3D, especially in medical imaging application is challenging in many aspects, mainly due to the 'curse of dimensionality' and limited available data set. The goal of this project is to develop an optimum strategy to scale GAN in 3D that generalizes well for conditional medical image synthesis and cross-modality translation. The student will be provided with all-round support including good research environment, sufficient computational resources and active guidance to make the thesis successful.
Supervisor:Suprosanna Shit
Director:Prof. Bjoern Menze
Student:
start-end: -
3D Human Pose Estimation from RGB Images (DA/MA/BA)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
Incremental Learning For Robotic Grasping (Master Thesis)

Supervisor:Fabian Manhardt
Director:Nassir Navab
Student:Pengyuan Wang
start-end: -
3D inpainting with Semantic Scene Completion (Master Thesis)

Existing semantic scene completion methods target at complete missing geometry and predict semantic meaning in 3D reconstruction, but without predicting the texture of the completed regions. This project aims to fill this gap by introducing a method to also predict the texture of the predicted regions.
Supervisor:Shun-Cheng Wu
Director:Federico Tombari
Student:
start-end: -
Deep Generative Model for Longitudinal Analysis (Master Thesis)

Longitudinal analysis of a disease is an important issue to understand its progression as well as to design prognosis and early diagnostic tools. From the longitudinal sample series where data is collected from multiple time points, both the spatial structural abnormalities and the longitudinal variations are captured. Therefore, the temporal dynamics of a disease are more informative than static observations of the symptoms, in particular for neuro-degenerative diseases whose progression span over years with early subtle changes. In this project, we will develop a deep generative method to model the lesion progression over time.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Continual and incremental learning with less forgetting strategy (Master Thesis)

Recently, deep learning has great success in various applications such as image recognition, object detection, and medical applications, etc. However, in the real world deployment, the number of training data (sometimes the number of tasks) continues to grow, or the data cannot be given at once. In other words, a model needs to be trained over time with the increase of the data collection in a hospital (or multiple hospitals). A new type of lesion could be also defined by medical experts. Then, the pre-trained network needs to be further trained to diagnose these new types of lesions with increased data. ‘Class-incremental learning’ is a research area that aims at training the learned model to add new tasks while retaining the knowledge acquired in the past tasks. It is challenging because DNNs are easy to forget previous tasks when learning new tasks (i.e. catastrophic forgetting). In real-world scenarios, it is difficult to store all training data which was used when training DNN at the previous time due to the privacy issues of medical data. In this project, we will develop a solution to this problem in medical applications by investigating an effective and novel learning method.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:Afshar Kakaei
start-end: -
Multiple sclerosis lesion segmentation from Longitudinal brain MRI (IDP)

Longitudinal medical data is defined that imaging data are obtained at more than one time-point where subjects are scanned repeatedly over time. Longitudinal medical image analysis is a very important topic because it can solve some difficulties which are limited when only spatial data is utilized. Temporal information could provide very useful cues for accurately and reliably analyzing medical images. To effectively analyze temporal changes, it is required to segment region-of-interest accurately in a short time. In the series of images acquired over multiple times of imaging, available cues for segmentation become richer with the intermediate predictions. In this project, we will investigate a way to fully exploit this rich source of information.
Supervisor:Dr. Seong Tae Kim, Ashkan Khakzar
Director:Prof. Dr. Nassir Navab
Student:Moiz Sajid, Stefan Denner
start-end: -
Neural solver for PDEs (Hiwi)

Supervisor:Suprosanna Shit
Director:Bjoern Menze
Student:
start-end: -
Robust training of neural networks under noisy labels (Master Thesis)

The performance of supervised learning methods highly depends on the quality of the labels. However, accurately labeling a large number of datasets is a time-consuming task, which sometimes results in mismatched labeling. When the neural networks are trained with noisy data, it might be biased to the noisy data. Therefore the performance of the neural networks could be poor. While label noise has been widely studied in the machine learning society, only a few studies have been reported to identify or ignore them during the process of training. In this project, we will investigate the way to train the neural network under noisy data robustly. In particular, we will focus on exploring effective learning strategies and loss correction methods to address the problem.
Supervisor:Dr. Seong Tae Kim, Dr. Shadi Albarqouni
Director:Prof. Dr. Nassir Navab
Student:Cagri Yildiz
start-end: -
Self-supervised learning for out-of-distribution detection in medical applications (Master Thesis)

Although recent neural networks have achieved great successes when the training and the testing data are sampled from the same distribution, in real-world applications, it is unnatural to control the test data distribution. Therefore, it is important for neural networks to be aware of uncertainty when new kinds of inputs (which is called out-of-distribution) are given. In this project, we consider the problem of out-of-distribution detection in neural networks. In particular, we will develop a novel self-supervised learning approach for out-of-distribution detection in medical applications.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:Abinav Ravi Venkatakrishnan
start-end: -
Investigation of Interpretation Methods for Understanding Deep Neural Networks (Project)

Machine learning and deep learning has made breakthroughs in many applications. However, the basis of their predictions is still difficult to understand. Attribution aims at finding which parts of the network’s input or features are the most responsible for making a certain prediction. In this project, we will explore the perturbation-based attribution methods.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Spatio-temporal deep network for early disease detection (Project)

Alzheimer’s disease (AD) is one of the serious neurological diseases which is defined by slowly progressive memory loss and cognitive deficits. Detecting AD in an early stage is highly important for exerting possible interventions to delay its progression. To detect AD in the early stage, it is important to accurately analyze spatio-temporal patterns and changes in longitudinal medical data. In this project, we will develop a method to effectively detect AD/MCI from longitudinal brain scans. In particular, deep neural architecture and effective training strategy under the limited number of longitudinal scans would be explored.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:Borja Sánchez Clemente
start-end: -
Scene graph generation (Master Thesis)

We are looking for a motivated student to work on a research topic that involves deep learning and scene understanding. The project consists on generating scene graphs which is a compact data representation that describes an image or 3D model of a scene. Each node of this graph represents an object, while the edges represent relationships/interactions between these objects, e.g. "boy - holding - racket" or "cat - next to - tree". The application of scene graphs involve image generation content-based queries for image search, and sometimes serve as additional context to improve object detection accuracy. Preferably master thesis. Also possible as guided research.
Supervisor:Helisa Dhamo
Director:Federico Tombari
Student:
start-end: -
Development of spatio-temporal segmentation model for tumor volume calculation in micro-CT (Master Thesis)

To develop a spatio-temporal segmentation model where the network is exposed to previous temporal information and builds this complex mapping to segment a given mouse micro-CT image to allow accurate tumor volume calculations. A dataset with micro-CT scans of over 69 mice with repeat imaging is available with ground truth annotations. Mice were either treated with radiotherapy or left untreated. The small animal data act as a surrogate for clinical datasets treated with MR-linac technology, which requires automatic spatio-temporal segmentation.
Supervisor:Dr. Shadi Albarqouni, Dr. Seong Tae Kim, Dr. Guillaume Landry
Director:Prof. Dr. Nassir Navab
Student:Tetiana Klymenko
start-end: -
Trajectory Validation using Deep Learning Methods (Master Thesis)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
Unsupervised Brain Representation Learning (Master Thesis)

In this project, we investigate a method to learn an unsupervised representation of the brain by exploiting the transformation equivariance and interpretability properties of capsules networks. The unsupervised representations obtained by our network are evaluated by performing downstream classification and anomaly detection tasks on the latent space. In addition, we will further explore the feature disentanglement and image generation capabilities of our network.
Supervisor:Dr. Seong Tae Kim, Matthias Keicher
Director:Prof. Dr. Nassir Navab
Student:
start-end: -

Finished Theses



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