An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes - Thamm, Florian; Jürgens, Markus; Taubmann, Oliver; Thamm, Aleksandra; Rist, Leonhard; Ditt, Hendrik; Maier, Andreas (Biomedical Physics and Engineering Express, 2022)
During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often enabled by Computed Tomography Angiography (CTA). Still, the identification and analysis of the cerebral arteries remain time consuming in such scans due to a large number of peripheral vessels which may disturb the visual impression. In previous work we proposed VirtualDSA++, an algorithm designed to segment and label the cerebrovascular tree on CTA scans. Especially with stroke patients, labeling is a delicate procedure, as in the worst case whole hemispheres may not be present due to impeded perfusion. Hence, we extended the labeling mechanism for the cerebral arteries to identify occluded vessels. In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92 % and 95 %. To the best of our knowledge, ours is the first work to address labeling and occlusion detection at once, whereby a sensitivity of 67 % and a specificity of 81 % were obtained for the latter. VirtualDSA++ also automatically segments and models the intracranial system, which we further used in a deep learning driven follow up work. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features. Exemplary, we derive in detail, firstly, the interactive planning of vascular interventions like the mechanical thrombectomy and secondly, the interactive suppression of vessel structures that are not of interest in diagnosing strokes (like veins). We discuss both features as well as further possibilities emerging from the proposed concept.<br

Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection - Thamm, Florian; Taubmann, Oliver; Jürgens, Markus; Thamm, Aleksandra; Denzinger, Felix; Rist, Leonhard; Ditt, Hendrik; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022)
Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, accurate and automated diagnosis of these scans is desirable. Human readers compare the left and right hemispheres in their assessment of strokes. A large training data set is required for a standard deep learning-based model to learn this strategy from data. As labeled medical data in this field is rare, other approaches need to be developed. To both include the prior knowledge of side comparison and increase the amount of training data, we propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres or hemisphere subregions from different patients. The subregions cover vessels commonly affected by LVOs, namely the internal carotid artery (ICA) and middle cerebral artery (MCA). In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres. Furthermore, we propose an extension of that architecture to process the individual hemisphere subregions. All configurations predict the presence of an LVO, its side, and the affected subregion. We show the effect of recombination as an augmentation strategy in a 5-fold cross validated ablation study. We enhanced the AUC for patient-wise classification regarding the presence of an LVO of all investigated architectures. For one variant, the proposed method improved the AUC from 0.73 without augmentation to 0.89. The best configuration detects LVOs with an AUC of 0.91, LVOs in the ICA with an AUC of 0.96, and in the MCA with 0.91 while accurately predicting the affected side.<br
Keywords: Computed Tomography Angiography; Stroke; Augmentation

Deep learning based drill wear segmentation and analysis of the wear progress - Thamm, Aleksandra; Thamm, Florian; Wiedemann, Markus; Bretschneider, Jochen; Sikorska, Magdalena; Maier, Andreas (International Journal on Interactive Design and Manufacturing, 2024)
Cutting tools and their condition monitoring play a major role in the world of manufacturing. The ultimate goal is to strategically prolong the intervals between tool replacements, optimizing cutting times and consequently minimizing costs. Achieving this requires precise quantification of tool wear, a process traditionally marked by its time-intensive and laborious nature. An automatic drill wear detection for two types of a damage (flank and corner wear) has been developed. This approach utilizes deep learning, specifically the nnU-Net framework. The model has been tested using a five-fold cross-validation approach, and the results are promising. This approach has been assessed by comparing the ground truths, which were manually annotated by experts, with the network’s predictions. For flank wear, a Dice Similarity Coefficient (DSC) of 0.93 and an Intersection over Union (IoU) of 0.87 has been achived. For outer corner wear, a DSC of 0.95 and an IoU of 0.90 has been attained. The whole wear segmentation yielded a DSC of 0.93 and an IoU of 0.87. With this presented method, the user automatically receives information about the degree of the two types of wear. Measurement of the wear width is no longer necessary. This method can assists in the development of wear parameters over time, commonly referred to as the wear-tool life curve. This curve optimization enables precise adjustment of cutting parameters, encompassing machine settings, environmental conditions, workpiece materials, and machine tool characteristics for future batches of workpieces. Additionally, this information serves as a foundation for empirical modeling of wear progression.
CRIS-Team Scopus Importer:2024-11-01
Keywords: Deep leaning; Drill; Machine tools; nnU-net; Segmentation

Failure and Risk Analysis based on Maintenance Reports of Machines Components in Manufacturing Industry - Thamm, Aleksandra; Herz, Matthias; Wiedemann, Markus; Thamm, Florian; Maier, Andreas (XIII. International Conference on the Theory of Machines and Mechanisms - TMM2020, 2021)
Manufacturing engineering is concerned with the application of engineering practices in manufacturing processes and production methods. Continuous optimization of processes, tools as well as machinery and equipment is the key to producing quality products with optimal capital investment. Hereby, maintenance plays a crucial role and allows manufacturers to optimize their processes. To enable improvements in this field it is required to analyze and evaluate the necessities for actions on component level. In this work we want to present a detailed retrospective analysis of more than 4.000 maintenance reports and analyze causes for assembly failures depending on the associated components. Furthermore, we extend the analysis and investigate the failure dependency with respect to the days elapsed from the date of maintenance request to resolving the issue. It turned out, not all cases with high failure rates are associated with subsequent higher risks considering the respective urgency related to each failure case. Furthermore, we discovered especially hydraulic and pneumatic components have a significant high to critical risk, as the labour safety and other components are threatened by their failure. Moreover we determined, that only 10% of all actions were planned maintenance and thus we propose based on the results of the risks analysis, how planned and preventive maintenance should be extended to reduce costs in this regard.<br
Keywords: Manufacturing; Failure Analysis; Risk Analysis; Spare Parts; Maintenance; Preventive Maintenance

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach - Thamm, Aleksandra; Thamm, Florian; Sawodny, Annette; Zeitler, Sally; Merklein, Marion; Maier, Andreas (Materials, 2023)
An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO 12004-2, is a wide-spread procedure for evaluating the formability of sheet metal materials. Thereby the FLC is affected by influences originating from intrinsic factors of the Nakajima test-setup, such as friction, which leads to deviations from the linear strain path, biaxial prestress and bending superposition. These disadvantages can be circumvented by an alternative test combination of uniaxial tensile test and hydraulic bulge test. In addition, the forming limit capacity of many lightweight materials is underestimated using the cross-section method according to DIN EN ISO 12004-2, due to the material-dependent occurrence of multiple strain maxima during forming or sudden cracking without prior necking. In this regard, machine learning approaches have a high potential for a more accurate determination of the forming limit curve due to the inclusion of other parameters influencing formability. This work presents a machine learning approach focused on uniaxial tensile tests to define the forming limit of lightweight materials and high-strength steels. The transferability of an existing weakly supervised convolutional neural network (CNN) approach was examined, originally designed for Nakajima tests, to uniaxial tensile tests. Additionally, a stereo camera-based method for this purpose was developed. In our evaluation, we train and test materials, including AA6016, DX54D, and DP800, through iterative data composition, using cross-validation. In the context of our stereo camera-based approach, strains for different materials and thicknesses were predicted. In this cases, our method successfully predicted the major strains with close agreement to ISO standards. For DX54D, with a thickness of 0.8 mm, the prediction was 0.659 (compared to ISO’s 0.664). Similarly, for DX54D, 2.0 mm thickness, the predicted major strain was 0.780 (compared to ISO 0.705), and for AA6016, at 1.0 mm thickness, a major strain of 0.314 (in line with ISO 0.309) was estimated. However, for DP800 with a thickness of 1.0 mm, the prediction yielded a major strain of 0.478 (as compared to ISO 0.289), indicating a divergence from the ISO standard in this particular case. These results in general, generated with the CNN stereo camera-based approach, underline the quantitative alignment of the approach with the cross-section metho
Keywords: forming limit curve; pattern recognition; sheet metal forming; machine learning