Index

Real-World Constrained Parameter Space Analysis for Rigid Head Motion Simulation

If you are interested in the topic, please send an email with your transcript of records and CV to: manuela.goldmann@fau.de

Description

This thesis aims to perform a conclusive analysis of the parameter space of rigid (6DOF) head motion patterns, obtained from measurements with an in-house optical tracking system integrated in a C-arm CT scanner at Siemens Healthineers in Forchheim. By analyzing the spatiotemporal correlations and constraints in the 6DOF parameter space, lower-dimensional underlying structures might be uncovered. Clustering techniques can be incorporated to further reveal sub-manifolds in the 6DOF space, as well as distinguishing different classes of motion types like breathing, nodding, etc. A Variational Autoencoder (or similar) should be trained with the goal of providing annotated synthetic datasets with realistic motion patterns.

Work Packages

Literature research on rigid transformations, manifold- and spatiotemporal correlation analysis, clustering techniques (optional), and Variational Autoencoders
Acquisition of representative head motion dataset via optical tracker; extraction of parameter times series
Analysis of spatiotemporal correlations and constraints in the 6DOF parameter space
Model implementation, training and evaluation in Python
Discussion of results using conclusive metrics and comparison to real clinical data

Design and Dataset Generation of Scanning Objects for CT Trajectory Optimization

Abstract:
This master thesis addresses the need for effective validation and optimization of computed tomography (CT) scan trajectories, crucial for industrial applications. The research focuses on designing and automating the creation of 3D scanning objects that can systematically test and verify the performance of trajectory optimization algorithms. The central research question explores how to design such objects using tools like Blender, while ensuring that these test scenarios are both efficient and scalable. A key goal is to generate a comprehensive dataset of these scanning objects, enabling the evaluation and comparison of various trajectory optimization methods.

Research Objectives:
1. Designing Scanning Objects: Establish a method for creating 3D objects in Blender that specifically target challenges faced in CT trajectory optimization, such as irregular geometries, material contrasts, and complex edge structures. These objects will serve as benchmarks for evaluating trajectory algorithms.

2. Dataset Creation for Trajectory Evaluation: One of the core deliverables of this thesis is to generate a standardized dataset of 3D objects. This dataset will enable comprehensive evaluation and comparison of different CT trajectory optimization algorithms, using metrics such as scan efficiency, image quality (measured by SSIM, PSNR), and artifact reduction.

3. Trajectory Optimization Validation: Evaluate CT trajectory optimization methods using the generated dataset. Simulate scan trajectories and validate algorithm performance based on the reconstructed image quality and optimization of scan time. Metrics such as structural similarity, noise reduction, and coverage of scan angles will be analyzed.

Leveraging Large Language Models for Scanner-Compatible CT Protocol Generation

Neural Network based classification on dynamic clouds: Integrating video analysis and time series monitoring data

Real-Time Traffic Sign Detection for Smart Data Logging

CZ_MT_Proposal_v2

Deep Learning for Geo-Referencing Historical Utility Documents With Geographical Features

Abstract:

The digitization of industries has spurred significant advancements across sectors, including utilities responsible for essential services like heating and water supply. As many utility systems developed before the digital era, they hold immense potential for optimization through digital representation. Accurate mapping of their extensive underground pipeline networks is key to improving operational efficiency. However, this digitization presents challenges, primarily because extracting geographic information from historical planning documents is difficult, as the infrastructure remains buried underground.

In this work, we propose a two-stage deep-learning framework to extract geographic information from historical utility planning records and facilitate the digital representation of utility networks. During the first stage, we frame this as a geo-location classification task, using a Convolutional Neural Network (CNN) to classify OpenStreetMap images into specific geographic regions covered by the utility network. In the second stage, we address the scarcity of annotated data by applying a style-transfer technique to historical documents containing geographic features, converting them into a format similar to OpenStreetMap images. This process enables further classification using the trained CNN. We will evaluate the method on real-world utility data.

This thesis is part of the “UtilityTwin” project.

Improving OCR for Structured Documents using Domain Knowledge

Thesis Description

The digitization of inventory cards is a recurring issue for museums and university collections. These cards hold structured data organized by individual layouts that need to be preserved when digitized. Optical Character Recognition (OCR) can be used for pure text recognition but struggles with structured content: The recognition accuracy decreases due to missing textual context and it lacks interpretation of the structured layout.

The goal of this thesis is to build a human-supported layout analysis for enabling OCR pipelines to convert inventory cards to structured data. The research aims to investigate whether OCR accuracy can be improved by incorporating prior knowledge regarding the structure and content of text fields.

Mandatory Goals:

Design UI Application with following capabilities:
- Card layout definition for template matching data fields
- Detection and correction of minor shifts and rotations
- Run OCR / Image Extraction and export to structured data (e.g. csv)

(Semi-)manually annotate data set for testing and fine-tuning (ca. 100 validation / 500 training set size)
Fine-tune one OCR pipeline on training samples + evaluate on validation split (baseline)
Re-train OCR pipeline with additional data type information (int, float, string) + evaluate in comparison to baseline
Additional approach: Baseline OCR with postprocessing steps
- Rule-based: ensure data consistency by category-specific rules, e.g. normalizing to default unit for weights (“12” -> “12 g”)
- LLM: Query ChatGPT / lab-internal LLM with OCR result and expected output data type, request correction of OCR output

Optional Goals:

Add other OCR pipelines for baseline comparison
Introduce and train for more specific data types: weight, date, currency, dimensions
Test different feature fusion approaches for incorporating the data type information
Compare to LLM-based approach to OCR as additional baseline

Real-time Path Loss Prediction Using Deep Learning for Smart Meter Communication System

Path Loss and Deep Learning:

Path loss measures the reduction in signal power between a transmitter and receiver. It plays a crucial role in determining the coverage and reliability of communication networks, especially in smart meter wireless communication systems. Therefore, accurate path loss prediction is essential for designing efficient networks and ensuring reliable data transfer. Traditional methods, such as empirical models [1] and deterministic approaches [2], when predicting path loss, faces limitations in generalizing across environments or suffer from high computational complexity. In contrast, machine learning and deep learning-based methods [3][4][5][6] offer a promising balance between accuracy and computational efficiency.

Thesis Outline:

This thesis aims to advance path loss prediction in smart meter systems, focusing specifically on LPWAN [7] communication technologies. While prior research has made significant strides, advancements in technology provide opportunities to improve model accuracy, reduce complexity, and enhance versatility. The primary goal is to develop and implement a robust deep learning model for real-time path loss prediction in a fixed-area network using existing smart meter data. Consequently, the outcomes of this work will be valuable for optimizing network management and enhancing the reliability of communication in smart meter deployments.

This thesis for path loss prediction using deep learning will cover the following aspects:

Conducting a comprehensive literature review,
Developing and implementing a machine learning or deep learning model for path loss prediction in a fixed network smart meter scenario, using available meter data,
Thoroughly evaluating the model’s performance in terms of accuracy, robustness, and real-time prediction capabilities.

References:

Evaluating the Performance of GAMS for Predicting Mortality Compared to Traditional Scoring Systems

External Supervision: Mr. Lasse Bohlen (University of Leipzig)

This master’s thesis provides a critical review of the applicability of Generalized Additive Models (GAMs) for mortality prediction in clinical practice and comparisons of GAMs with established scoring systems. The study also evaluates whether using GAMs can increase the rate of accurate prediction and, hence, improve the health decision-making process and subsequent patient care.
SAPS and APACHE are traditional scoring systems used in health care to determine mortality prognosis. However, they have yet to gain much understanding due to their stiffness and the conditions imposed on them. Such models often incorporate explicit variables and linear dependency, while patients’ data involves many interactions and non-linearity. [1]
GAMs are a more flexible alternative that permits the utilization of non-linear relations and interactions between covariates. Therefore, they are a very useful tool for providing new insights into clinical data patterns [2]. This thesis focuses on the modeling background of GAMs and investigates their predictive capability by applying them to clinical databases.

Research Objectives:

To investigate the ability of the GAMs to achieve a more accurate prediction of clinical mortality compared to the traditional scoring systems.
In this cross-sectional study, the difference in statistical accuracy and clinical applicability of GAMs will be discussed.
To offer the principles for the application of GAMs in the clinical setting.

Methodology:
The procedure includes building the new algorithm, which consists of GAMs, with the help of clinical databases and comparing it with the existing predictive scoring systems. Hence, evaluating models will involve using the auc-roc score, a statistical metric [3]. The study will employ the MIMIC-III clinical dataset [4].

Anticipated Impact:
Hence, by showing the utility of GAMs in enhancing mortality prediction, this study seeks to influence better and more personalized patient care approaches. As such, the findings inform the next research steps and the integration of higher-order prognostic models in the healthcare context.

References:
[1] Reza Sadeghi, Tanvi Banerjee, and William Romine. “Early hospital mortality prediction using vital signals”. In: Smart Health 9-10 (2018). CHASE 2018 Special Issue, pp. 265–274. ISSN: 2352-6483. DOI: https://doi.org/10.1016/j.smhl.2018.07.001. URL: https://www.sciencedirect.com/science/article/pii/S2352648318300357.
[2] Shima Moslehi et al. “Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran”. In: BMC Med Res Methodol 22.1 (2022), p. 339. DOI: 10.1186/s12874-022-01827-y.
[3] Shangping Zhao et al. “Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients.” In: Int J Gen Med 16 (2023). PMID: 37525648; PMCID: PMC10387249, pp. 3151–3161. DOI: 10.2147/IJGM.S391423.
[4] Rui Liu et al. “Predicting in-hospital mortality for MIMIC-III patients: A nomogram combined with SOFA score”. In: Medicine (Baltimore) 101.42 (2022), e31251. DOI: 10.1097/MD.0000000000031251.