Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation

Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time. To overcome this issue, we introduce a network structure for volumetric data without 3D convolutional layers. The main idea is to include maximum intensity projections from different directions to transform the volumetric data to a sequence of images, where each image contains information of the full data. We then apply 2D convolutions to these projection images and lift them again to volumetric data using a trainable reconstruction algorithm.The proposed network architecture has less storage requirements than network structures using 3D convolutions. For a tested binary segmentation task, it even shows better performance than the 3D U-net and can be trained much faster.Comment: presented at the SAMPTA 2019 conferenc

Entwicklung eines verbesserten Messprinzips für frequenzaufgelöste depolarisierte dynamische Lichtstreuung

Angermann C. Entwicklung eines verbesserten Messprinzips für frequenzaufgelöste depolarisierte dynamische Lichtstreuung. Bielefeld: Universität Bielefeld; 2017

Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks

Unsupervised image transfer enables intra- and inter-modality image translation in applications where a large amount of paired training data is not abundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a novel method for uni-directional domain mapping that does not rely on any paired training data. A proper transfer is achieved by using a GAN architecture and a novel generator loss based on patch invariance. To be more specific, the generator outputs are evaluated and compared at different scales, also leading to an increased focus on high-frequency details as well as an implicit data augmentation. This novel patch loss also offers the possibility to accurately predict aleatoric uncertainty by modeling an input-dependent scale map for the patch residuals. The proposed method is comprehensively evaluated on three well-established medical databases. As compared to four state-of-the-art methods, we observe significantly higher accuracy on these datasets, indicating great potential of the proposed method for unpaired image transfer with uncertainty taken into account. Implementation of the proposed framework is released here: \url{https://github.com/anger-man/unsupervised-image-transfer-and-uq}.Comment: Accepted to ECCV 2022 Workshop on Uncertainty Quantification for Computer Vision (UNCV 2022

Three-dimensional Bone Image Synthesis with Generative Adversarial Networks

Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data availability and privacy are hampering research progress and thus rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy, but also allows to \textit{draw} new patients with specific characteristics, enabling the development of data-driven models on a much larger scale. This work demonstrates that three-dimensional generative adversarial networks (GANs) can be efficiently trained to generate high-resolution medical volumes with finely detailed voxel-based architectures. In addition, GAN inversion is successfully implemented for the three-dimensional setting and used for extensive research on model interpretability and applications such as image morphing, attribute editing and style mixing. The results are comprehensively validated on a database of three-dimensional HR-pQCT instances representing the bone micro-architecture of the distal radius.Comment: Submitted to the journal Artificial Intelligence in Medicin

Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines

Digitalization offers a large number of promising tools for large internal combustion engines such as condition monitoring or condition-based maintenance. This includes the status evaluation of key engine components such as cylinder liners, whose inner surfaces are subject to constant wear due to their movement relative to the pistons. Existing state-of-the-art methods for quantifying wear require disassembly and cutting of the examined liner followed by a high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop nondestructive yet reliable methods for quantifying the surface condition. A deep-learning framework is proposed that allows computation of the bearing load curves from reflection RGB images of the liner surface that can be collected with a wide variety of simple imaging devices, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to predict the bearing load curve of the corresponding depth profile from the collected RGB images, which in turn can be used for further wear evaluation. Training of the network is performed using a custom-built database containing depth profiles and reflection images of liner surfaces of large gas engines. The results of the proposed method are visually examined and quantified considering several probabilistic distance metrics and comparison of roughness indicators between ground truth and model predictions. The observed success of the proposed method suggests its great potential for quantitative wear assessment on engines during service directly on site

Worsening calcification propensity precedes all-cause and cardiovascular mortality in haemodialyzed patients

A novel in-vitro test (T-50-test) assesses ex-vivo serum calcification propensity which predicts mortality in HD patients. The association of longitudinal changes of T-50 with all-cause and cardiovascular mortality has not been investigated. We assessed T-50 in paired sera collected at baseline and at 24 months in 188 prevalent European HD patients from the ISAR cohort, most of whom were Caucasians. Patients were followed for another 19 [interquartile range: 11-37] months. Serum T-50 exhibited a significant decline between baseline and 24 months (246 +/- 64 to 190 +/- 68 minutes;p < 0.001). With serum Delta-phosphate showing the strongest independent association with declining T-50 (r = -0.39;p < 0.001) in multivariable linear regression. The rate of decline of T-50 over 24 months was a significant predictor of all-cause (HR = 1.51 per 1SD decline, 95% CI: 1.04 to 2.2;p = 0.03) and cardiovascular mortality (HR = 2.15;95% CI: 1.15 to 3.97;p = 0.02) in Kaplan Meier and multivariable Cox-regression analysis, while cross-sectional T-50 at inclusion and 24 months were not. Worsening serum calcification propensity was an independent predictor of mortality in this small cohort of prevalent HD patients. Prospective larger scaled studies are needed to assess the value of calcification propensity as a longitudinal parameter for risk stratification and monitoring of therapeutic interventions