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163 results on '"Ditt, Hendrik"'

1. Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields

Author

Rist, Leonhard, Stephan, Pluvio, Maul, Noah, Vorberg, Linda, Ditt, Hendrik, Sühling, Michael, Maier, Andreas, Egger, Bernhard, and Taubmann, Oliver

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Hence, various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. However, there is no versatile framework to flatten complex sparse structures including vascular, duct or bone systems. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image. We further propose distortion regularization strategies and combine geometric with intensity-based loss formulations to also display non-annotated and auxiliary targets. In addition to improved versatility, our unfolding technique outperforms mesh-based baselines for sparse structures w.r.t. peak distortion and our regularization scheme yields smoother transformations compared to Jacobian formulations from neural field-based image registration.

Published
2024

2. Robust Feature Selection for Classifying Early Ischemic Changes in Posterior Stroke

Author

Rist, Leonhard, Ditt, Hendrik, Sühling, Michael, Honus, Valentin, Schramm, Peter, Maier, Andreas, Taubmann, Oliver, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Su, Ruisheng, editor, de la Rosa, Ezequiel, editor, Rist, Leonhard, editor, Heylen, Ewout, editor, te Nijenhuis, Frank, editor, Ruijters, Danny, editor, Schirmer, Markus D., editor, McKinley, Richard, editor, Wegener, Susanne, editor, Wiest, Roland, editor, and van Walsum, Theo, editor

Published
2025
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3. Usefulness of Monoenergetic Non-contrast CT and X-Map Images for Deep Learning-Based Stroke Lesion Segmentation

Author

Vorberg, Linda, Ditt, Hendrik, Sühling, Michael, Maier, Andreas, Murray, Nicolas, Nicolaou, Savvas, Taubmann, Oliver, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Su, Ruisheng, editor, de la Rosa, Ezequiel, editor, Rist, Leonhard, editor, Heylen, Ewout, editor, te Nijenhuis, Frank, editor, Ruijters, Danny, editor, Schirmer, Markus D., editor, McKinley, Richard, editor, Wegener, Susanne, editor, Wiest, Roland, editor, and van Walsum, Theo, editor

Published
2025
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4. Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation

Author

Vorberg, Linda, Ditt, Hendrik, Sühling, Michael, Maier, Andreas, Murray, Nicolas, Nicolaou, Savvas, Taubmann, Oliver, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Su, Ruisheng, editor, de la Rosa, Ezequiel, editor, Rist, Leonhard, editor, Heylen, Ewout, editor, te Nijenhuis, Frank, editor, Ruijters, Danny, editor, Schirmer, Markus D., editor, McKinley, Richard, editor, Wegener, Susanne, editor, Wiest, Roland, editor, and van Walsum, Theo, editor

Published
2025
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7. Analysing Variables for 90-Day Functional-Outcome Prediction of Endovascular Thrombectomy

Author

Rodríguez-Salas, Dalia, Riess, Christian, Vicario, Celia Martín, Taubmann, Oliver, Ditt, Hendrik, Schwab, Stefan, Dörfler, Arnd, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yap, Moi Hoon, editor, Kendrick, Connah, editor, Behera, Ardhendu, editor, Cootes, Timothy, editor, and Zwiggelaar, Reyer, editor

Published
2024
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9. Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection

Author

Thamm, Florian, Taubmann, Oliver, Jürgens, Markus, Thamm, Aleksandra, Denzinger, Felix, Rist, Leonhard, Ditt, Hendrik, and Maier, Andreas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

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., Comment: PrePrint - Accepted at MICCAI 2022

Published
2022

10. An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes

Author

Thamm, Florian, Jürgens, Markus, Taubmann, Oliver, Thamm, Aleksandra, Rist, Leonhard, Ditt, Hendrik, and Maier, Andreas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Quantitative Biology - Quantitative Methods
Abstract

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.

Published
2022

13. Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations

Author

Thamm, Florian, Taubmann, Oliver, Jürgens, Markus, Ditt, Hendrik, and Maier, Andreas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Computed Tomography Angiography is a key modality providing insights into the cerebrovascular vessel tree that are crucial for the diagnosis and treatment of ischemic strokes, in particular in cases of large vessel occlusions (LVO). Thus, the clinical workflow greatly benefits from an automated detection of patients suffering from LVOs. This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data. Using only masks as the input to our model uniquely allows us to apply such deformations much more aggressively than one could with conventional image volumes while retaining sample realism. The neural network classifies the presence of an LVO and the affected hemisphere. In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets. Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0.85 from a baseline value of 0.56 using no augmentation. The best performance was achieved using a 3D-DenseNet yielding an AUC of 0.87. The augmentation had positive impact in classification of the affected hemisphere as well, where the 3D-DenseNet reached an AUC of 0.93 on both sides., Comment: 7 pages. Accepted at BVM-Workshop 2022, Springer

Published
2021

14. Flexible Unfolding of Circular Structures for Rendering Textbook-Style Cerebrovascular Maps

Author

Rist, Leonhard, Taubmann, Oliver, Ditt, Hendrik, Sühling, Michael, Maier, Andreas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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15. Detection of Pulmonary Embolisms in NCCT Data Using nnDetection

Author

Vorberg, Linda, Thamm, Florian, Ditt, Hendrik, Horger, Marius, Hagen, Florian, Maier, Andreas, Gesellschaft für Informatik e.V. (GI), Deserno, Thomas M., editor, Handels, Heinz, editor, Maier, Andreas, editor, Maier-Hein, Klaus, editor, Palm, Christoph, editor, and Tolxdorff, Thomas, editor

Published
2023
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20. Thrombus Detection in Non-contrast Head CT Using Graph Deep Learning

Author

Popp, Antonia, Taubmann, Oliver, Thamm, Florian, Ditt, Hendrik, Maier, Andreas, Breininger, Katharina, Gesellschaft für Informatik e.V. (GI), Maier-Hein, Klaus, editor, Deserno, Thomas M., editor, Handels, Heinz, editor, Maier, Andreas, editor, Palm, Christoph, editor, and Tolxdorff, Thomas, editor

Published
2022
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21. SyNCCT: Synthetic Non-contrast Images of the Brain from Single-Energy Computed Tomography Angiography

Author

Thamm, Florian, Taubmann, Oliver, Denzinger, Felix, Jürgens, Markus, Ditt, Hendrik, Maier, Andreas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, de Bruijne, Marleen, editor, Cattin, Philippe C., editor, Cotin, Stéphane, editor, Padoy, Nicolas, editor, Speidel, Stefanie, editor, Zheng, Yefeng, editor, and Essert, Caroline, editor

Published
2021
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23. Detection of Ischemic Infarct Core in Non-contrast Computed Tomography

Author

Hornung, Maximilian, Taubmann, Oliver, Ditt, Hendrik, Menze, Björn, Herman, Pawel, Fransén, Erik, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Mingxia, editor, Yan, Pingkun, editor, Lian, Chunfeng, editor, and Cao, Xiaohuan, editor

Published
2020
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31. A novel fully automated method for measuring ASPECTS to improve stroke diagnosis: Comparison to traditional ASPECTS.

Author

Gohla, Georg, Schwarz, Ricarda, Bier, Georg, Estler, Arne, Bongers, Malte N., Ditt, Hendrik, Fritz, Jan, Kemmling, André, Ernemann, Ulrike, and Horger, Marius

Subjects
STROKE, CEREBRAL infarction, INTERNAL carotid artery, ISCHEMIC stroke, CEREBRAL arteries, BLOOD volume
Abstract

Background and Purpose: To compare the accuracy of subjective Alberta Stroke Program Early CT Score (sASPECTS) evaluation and that of an automated prototype software (aASPECTS) on nonenhanced CT (NECT) in patients with early anterior territory stroke and controls using side‐to‐side quantification of hypoattenuated brain areas. Methods: We retrospectively analyzed the NECT scans of 42 consecutive patients with ischemic stroke before reperfusion and 42 controls using first sASPECTS and subsequently aASPECTS. We assessed the differences in Alberta Stroke Program Early CT Score (ASPECTS) and calculated the sensitivity and specificity of NECT with CT perfusion, whereas cerebral blood volume (CBV) served as the reference standard for brain infarction. Results: The clot was located in the middle cerebral artery (MCA) in 47.6% of cases and the internal carotid artery (ICA) in 28.6% of cases. Ten cases presented combined ICA and MCA occlusions. The stroke was right sided in 52.4% of cases and left sided in 47.6%. Reader‐based NECT analysis yielded a median sASPECTS of 10. The median CBV‐based ASPECTS was 7. Compared to the area of decreased CBV, sASPECTS yielded a sensitivity of 12.5% and specificity of 86.8%. The software prototype (aASPECTS) yielded an overall sensitivity of 65.5% and a specificity of 92.2%. The interreader agreement for ASPECTS evaluation of admission NECT and follow‐up CT was almost perfect (κ =.93). The interreader agreement of the CBV color map evaluation was substantial (κ =.77). Conclusions: aASPECTS of NECT can outperform sASPECTS for stroke detection. [ABSTRACT FROM AUTHOR]

Published
2024
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34. SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images

Author

Urschler, Martin, Bauer, Joachim, Ditt, Hendrik, Bischof, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Beichel, Reinhard R., editor, and Sonka, Milan, editor

Published
2006
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35. Automatic Point Landmark Matching for Regularizing Nonlinear Intensity Registration: Application to Thoracic CT Images

Author

Urschler, Martin, Zach, Christopher, Ditt, Hendrik, Bischof, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Larsen, Rasmus, editor, Nielsen, Mads, editor, and Sporring, Jon, editor

Published
2006
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48. VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data

Author

Thamm, Florian, Jürgens, Markus, Ditt, Hendrik, and Maier, Andreas

Subjects
Image segmentation, Human centered computing, Computing methodologies, Search with partial observations, Visualization
Abstract

Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA)., Eurographics Workshop on Visual Computing for Biology and Medicine, Vascular and Flow, 151, 155, Florian Thamm, Markus Jürgens, Hendrik Ditt, and Andreas Maier, CCS Concepts: Computing methodologies --> Image segmentation; Search with partial observations; Human-centered computing --> Visualization

Published
2020
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