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

Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients - Rist, Leonhard; Taubmann, Oliver; Thamm, Florian; Ditt, Hendrik; Sühling, Michael; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2022)
Purpose Vessel labeling is a prerequisite for comparing cerebral vasculature across patients, e.g., for straightened vessel examination or for localization. Extracting vessels from computed tomography angiography scans may come with a trade-off in segmentation accuracy. Vessels might be neglected or artificially created, increasing the difficulty of labeling. Related work mainly focuses on magnetic resonance angiography without stroke and uses trainable approaches requiring costly labels. Methods We present a robust method to identify major arteries and bifurcations in cerebrovascular models generated from existing segmentations. To localize bifurcations of the Circle of Willis, candidate paths for the adjacent vessels of interest are identified using registered landmarks. From those paths, the optimal ones are extracted by recursively maximizing an objective function for all adjacent vessels starting from a bifurcation to avoid erroneous paths and compensate for stroke. Results In 100 CTA stroke data sets for evaluation, 6 bifurcation locations are placed correctly in 85% of cases; 92.5% when allowing a margin of 5 mm. On average, 14 vessels of interest are found in 90% of the cases and traced correctly end-to-end in 73.5%. The baseline achieves similar detection rates but only 35.5% of the arteries are traced in full. Conclusion Formulating the vessel labeling process as a maximization task for bifurcation matching can vastly improve accurate vessel tracing. The proposed algorithm only uses simple features and does not require expensive training dat

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

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks - Rivera Monroy, Luis Carlos; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.<br

Detecting Large Vessel Occlusions using Graph Deep Learning - Kassam, Jad; Thamm, Florian; Rist, Leonhard; Taubmann, Oliver; Maier, Andreas (GeoMedIA 2022 Workshop Submission, 2022)
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Employing Graph Representations for Cell-Level Characterization of Melanoma MELC Samples - Rivera Monroy, Luis Carlos; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular level characterization of the tissue is then represented as a graph and finally used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87 %, outperforming existing approaches by 10 %.<br

Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps - Rist, Leonhard; Taubmann, Oliver; Ditt, Hendrik; Sühling, Michael; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023)
Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease, e.g., for fast assessment of vascular topology and lumen in stroke patients. Unfolding the brain vasculature into a 2D overview is, however, infeasible using common Curved Planar Reformation (CPR) due to the circular structure of the Circle of Willis (CoW) and the spatial configuration of the vessels typically rendering them unsuitable for mapping onto simple geometric primitives. We propose CeVasMap, extending the As-Rigid-As-Possible (ARAP) deformation by a smart initialization of the required mesh to map the CoW as well as a merging of neighboring vessels depending on the resulting degree of distortion. Otherwise, vessels are unfolded and attached individually, creating a textbook-style overview image. We provide an extensive distortion analysis, comparing the vector fields of individual and merged unfoldings of each vessel to their CPR results. In addition to enabling unfolding of circular structures, our method is on par in terms of incurred distortions to optimally oriented CPRs for individual vessels and comparable to unfavorable CPR orientations when merging the complete CoW with a median distortion of 65 μm/m
Keywords: Vessel unfolding; Distortion; Cerebrovascular disease

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features - Rivera Monroy, Luis Carlos; Rist, Leonhard; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2024)
Purpose This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles. Methods We utilized GNNs to process multiple pathological slides as cell-level graphs, comparing their performance with XGBoost and Random Forest classifiers. The analysis included two feature types: multi-stain profiles and Radiomic features. Dimensionality reduction techniques such as UMAP and t-SNE were applied to optimize the feature space, and graph connectivity was based on spatial and feature closeness. Results Integrating Radiomic features into spatially connected graphs significantly improved classification accuracy over traditional models. The application of UMAP further enhanced the performance of GNNs, particularly in classifying diseases with similar pathological features. The GNN model outperformed baseline methods, demonstrating its robustness in handling complex histopathological data. Conclusion Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study’s findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectivenes
Keywords: Radiomics; Multi-array imaging; Graph neural network; MELC; Histopathological analysis

Multi-level cancer profiling through joint cell-graph representations - Rivera Monroy, Luis Carlos; Rist, Leonhard; Wilm, Frauke; Ostalecki, Christian; Baur, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (Smart Health, 2024)
Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of , and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining technique
Keywords: Fluorescence microscopy imageGraph deep learningMultiplex imagingDigital pathology

Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning - Wang, Tianyi; Liu, Chang; Rist, Leonhard; Maier, Andreas (Bildverarbeitung für die Medizin 2024. BVM 2024, 2024)
While deep learning models are known to be able to solve the task of multi-organ segmentation, the scarcity of fully annotated multi-organ datasets poses a significant obstacle during training. The 3D volume annotation of such datasets is expensive, time-consuming and varies greatly in the variety of labeled structures. To this end, we propose a solution that leverages multiple partially annotated datasets using disentangled learning for a single segmentation model. Dataset-specific encoder and decoder networks are trained, while a joint decoder network gathers the encoders’ features to generate a complete segmentation mask. We evaluated our method using two simulated partially annotated datasets: one including the liver, lungs and kidneys, the other bones and bladder. Our method is trained to segment all five organs achieving a dice score of 0.78 and an IoU of 0.67. Notably, this performance is close to a model trained on the fully annotated dataset, scoring 0.80 in dice score and 0.70 in IoU respectively.
CRIS-Team Scopus Importer:2024-03-29

Pancreatic Vessel Landmark Detection in CT Angiography Using Prior Anatomical Knowledge - Rist, Leonhard; Homm, Christopher; Lades, Félix; Hernandez, Abraham Ayala; Sühling, Michael; Gudman Steuble Brandt, Erik; Maier, Andreas; Taubmann, Oliver (Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine, 2024)
Localizing vessels or their defining bifurcations is a frequent clinical problem for advanced visualizations in pancreatic cancer invasion analysis, driving the demand for design guidelines of easy-to-implement landmark detection solutions. When transforming such landmarks appropriately to surrogate targets, the competitive nnDetection and nnU-Net frameworks provide such solutions, especially for small data settings. Here, the underlying networks can further benefit from incorporating additional anatomical information. We present results on two CTA datasets consisting of arterial and venous phase images with 6 and 4 bifurcation landmarks surrounding the pancreas respectively. Landmark points were modeled as spheres to allow for the application of object detection/segmentation models. We evaluate both nn-frameworks for these tasks focusing on the incorporation of anatomical knowledge. Postprocessing nnDetection predictions with organ masks and landmark relation constraints boosts detection accuracies from 66.7 % to 79.4 % in the more challenging venous case and decreases the mean radial error from 9.06 to 4.92 mm. The nnU-Net benefits more from organ masks in the input when targeting problematic vessels, lowering the mean radial error from 12.97 to 8.45 mm when using the splenic mask for the venous task. Both networks have good initial detection rates for the arterial phase which are slightly boosted using our method to 93.7 % (nnU-Net) and 95.5 % (nnDetection). All remaining mispredictions are within the vessel of interest and thus sufficient for many downstream task

Projective Invariants for Geometric Calibration in Flat-Panel Computed Tomography - Aichert, André; Bier, Bastian; Rist, Leonhard; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.projec_8

Robust Feature Selection for Classifying Early Ischemic Changes in Posterior Stroke - Rist, Leonhard; Ditt, Hendrik; Sühling, Michael; Honus, Valentin; Schramme, Peter; Maier, Andreas; Taubmann, Oliver (Image Analysis in Stroke Diagnosis and Interventions, 2025)
Fast assessment in stroke diagnosis is essential to improve the treatment outcome. Scoring systems such as the ASPECT score facilitate the triage of patients according to stroke severity. For occlusions in the posterior circulation, pcASPECTS evaluates whether certain posterior cerebral regions show early signs of stroke and can be applied using the admission NCCT scan. This work investigates the automatic classification of early stroke changes in the two largest posterior regions based on NCCT images. The main focus lies on the implementation of robust measures that can counteract noise and scanner variances since they are harmful to established Radiomics pipelines. For 170 respectively derived regions from 85 patients, the described pipeline can reach up to 83.84% AUC with 79.40% sensitivity for the cerebellum and 73.14% AUC with 57.97% sensitivity for the occipital regions. A simple in-patient normalization scheme proved to be the most effective measure by improving the AUC by +8.17% and the sensitivity by +16.80%. Additional robustness techniques such as noise augmentation or discarding unstable and correlated features using the post-treatment scan resulted in only slight deviations from the best result, making them valuable tools for improving robustness when using Radiomics for posterior stroke classificatio

Segmentation of the Carotid Lumen and Vessel Wall using Deep Learning and Location Priors - Thamm, Florian; Denzinger, Felix; Rist, Leonhard; Martín Vicario, Celia; Kordon, Florian; Maier, Andreas (2021)
Keywords: Vessel Wall Segmentation; Magnetic Resonance Imaging; Deep Learning

Spatial Lesion Graphs: Analyzing Liver Metastases with Geometric Deep Learning for Cancer Survival Regression - Rist, Leonhard; Taubmann, Oliver; Muhlberg, Alexander; Denzinger, Felix; Thamm, Florian; Sühling, Michael; Norenberg, Dominik; Holch, Julian Walter; Maurus, Stefan; Gebauer, Leonie; Huber, Thomas; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
The degree of metastatic progress is a crucial factor in cancer assessment and is often estimated from few target lesions. However, features of the overall configuration of metastases, such as spread distance in the liver, offer further predictive information. Graphs can represent both local characteristics of individual lesions and their global spatial distribution. In this work, liver metastases stemming from a primary pancreatic tumor are transformed patient-wise into spatial lesion graphs and processed by geometric deep learning to estimate hazard ratios. The influence of lesion features and graph topologies is investigated in a data set of 78 patients to provide model design guidelines. Graph attention and differential pooling networks were selected as architectures to capitalize on the proposed topologies and provide meaningful visualizations of the decision process. Compared with conventional survival analysis methods and deep learning models, both networks show improvement with a maximum concordance of 68.9 %.
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Keywords: cancer; Geometric deep learning; graphs; liver; metastasis; multi-focal diseases; survival analysis