Prof. Mattias P. Heinrich Profile

Prof. Mattias P. Heinrich

at Univ zu Lübeck

SPIE Involvement:

Author

Publications (15)

Proceedings Article | 3 April 2023 Presentation + Paper

Weakly supervised airway orifice segmentation in video bronchoscopy

Ron Keuth, Mattias Heinrich, Martin Eichenlaub, Marian Himstedt

Proceedings Volume 12464, 124640A (2023) https://doi.org/10.1117/12.2654229

KEYWORDS: Image segmentation, Bronchoscopy, In vivo imaging, Video, Data modeling, Binary data, Tissues, Semantics

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Proceedings Article | 3 April 2023 Poster + Paper

Highly accurate deep registration networks for large deformation estimation in compression ultrasound

Laura F. Graf, Hanna Siebert, Sven Mischkewitz, Ron Keuth, Mattias Heinrich

Proceedings Volume 12464, 1246426 (2023) https://doi.org/10.1117/12.2653870

KEYWORDS: Image segmentation, Ultrasonography, Deformation, Image registration, Machine learning, Feature extraction, Convex optimization, Veins

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SPIE Journal Paper | 14 July 2022

Learning-based three-dimensional registration with weak bounding box supervision

Mona Schumacher, Hanna Siebert, Andreas Genz, Ragnar Bade, Mattias Heinrich

JMI, Vol. 9, Issue 04, 044001, (July 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.4.044001

KEYWORDS: Image registration, Image segmentation, 3D image processing, Image processing, Medical imaging, Image processing algorithms and systems, Feature extraction, Magnetic resonance imaging, Kidney, Computed tomography

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Bridging the domain gap for medical image segmentation with multimodal MIND features

Christian Kruse, Mattias Heinrich

Proceedings Volume 12032, 1203231 (2022) https://doi.org/10.1117/12.2612041

KEYWORDS: Data modeling, Computed tomography, Image segmentation, Magnetic resonance imaging, Kidney, Spleen, Medical imaging, Liver, Feature extraction

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Proceedings Article | 4 April 2022 Poster + Paper

Supervised deep generation of high-resolution arterial phase computed tomography kidney substructure atlas

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Agnes Fogo, Raymond Harris, Mark de Caestecker, Jeffery Spraggins, Mattias Heinrich, Yuankai Huo, Bennett Landman

Proceedings Volume 12032, 120322S (2022) https://doi.org/10.1117/12.2608290

KEYWORDS: Kidney, Image registration, Computed tomography, Image segmentation, Diffusion, 3D modeling, Visualization, Machine learning, Spatial learning

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The Human BioMolecular Atlas Program (HuBMAP) provides an opportunity to contextualize findings across cellular to organ systems levels. Constructing an atlas target is the primary endpoint for generalizing anatomical information across scales and populations. An initial target of HuBMAP is the kidney organ and arterial phase contrast-enhanced computed tomography (CT) provides distinctive appearance and anatomical context on the internal substructure of kidney organs such as renal context, medulla, and pelvicalyceal system. With the confounding effects of demographics and morphological characteristics of the kidney across large-scale imaging surveys, substantial variation is demonstrated with the internal substructure morphometry and the intensity contrast due to the variance of imaging protocols. Such variability increases the level of difficulty to localize the anatomical features of the kidney substructure in a well-defined spatial reference for clinical analysis. In order to stabilize the localization of kidney substructures in the context of this variability, we propose a high-resolution CT kidney substructure atlas template. Briefly, we introduce a deep learning preprocessing technique to extract the volumetric interest of the abdominal regions and further perform a deep supervised registration pipeline to stably adapt the anatomical context of the kidney internal substructure. To generate and evaluate the atlas template, arterial phase CT scans of 500 control subjects are de-identified and registered to the atlas template with a complete end-to-end pipeline. With stable registration to the abdominal wall and kidney organs, the internal substructure of both left and right kidneys are substantially localized in the high-resolution atlas space. The atlas average template successfully demonstrated the contextual details of the internal structure and was applicable to generalize the morphological variation of internal substructure across patients.

Proceedings Article | 4 April 2022 Presentation + Paper

Opportunistic CT screening for degenerative deformities and osteoporotic fractures with 3D DeepLab

Hellena Hempe, Eren Yilmaz, Carsten Meyer, Mattias Heinrich

Proceedings Volume 12032, 120320K (2022) https://doi.org/10.1117/12.2612848

KEYWORDS: Computed tomography, Spine, Network architectures, Image segmentation, Visualization, Classification systems

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Proceedings Article | 15 February 2021 Presentation + Paper

DA-AR-Net: an attentive activation based Deformable auto-encoder for group-wise registration

Prateek Gupta, Hanna Siebert, Mattias Heinrich, Kumar Rajamani

Proceedings Volume 11596, 115960Q (2021) https://doi.org/10.1117/12.2582176

KEYWORDS: Image registration, Computer programming, Medical imaging, Data modeling, Visualization, Image compression, Feature extraction

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Proceedings Article | 15 February 2021 Presentation + Paper

Learning inverse consistent 3D groupwise registration with deforming autoencoders

Hanna Siebert, Kumar Rajamani, Mattias Heinrich

Proceedings Volume 11596, 115960F (2021) https://doi.org/10.1117/12.2581948

KEYWORDS: Image registration, Medical imaging, Data modeling, Computer programming, 3D modeling, Neuroimaging, Magnetic resonance imaging, Image restoration, Brain, 3D scanning

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Proceedings Article | 15 February 2021 Presentation

Development and characterization of a chest CT atlas

Kaiwen Xu, Riqiang Gao, Mirza Khan, Shunxing Bao, Yucheng Tang, Steve Deppen, Yuankai Huo, Kim Sandler, Pierre Massion, Mattias Heinrich, Bennett Landman

Proceedings Volume 11596, 115961G (2021) https://doi.org/10.1117/12.2580800

KEYWORDS: Chest, Lung cancer, Computed tomography, Chronic obstructive pulmonary disease, Tomography, Lung, Feature extraction, Databases, Cancer, Brain-machine interfaces

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Proceedings Article | 15 February 2021 Presentation + Paper

Construction of a multi-phase contrast computed tomography kidney atlas

Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes Fogo, Raymond Harris, Mark de Caestecker, Mattias Heinrich, Jeffery Spraggins, Yuankai Huo, Bennett Landman

Proceedings Volume 11596, 115961T (2021) https://doi.org/10.1117/12.2580561

KEYWORDS: Kidney, Computed tomography, Information visualization, Visualization, Tissues, Associative arrays, Abdomen

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Proceedings Article | 16 March 2020 Presentation + Paper

Segmentation of retinal low-cost optical coherence tomography images using deep learning

Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias Heinrich, Heinz Handels

Proceedings Volume 11314, 113141O (2020) https://doi.org/10.1117/12.2551324

KEYWORDS: Image segmentation, Optical coherence tomography, Retina, Computer programming, Signal to noise ratio, Convolution, Denoising, Eye, Imaging systems, Image quality

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Proceedings Article | 10 March 2020 Presentation + Paper

Weakly supervised pancreas segmentation based on class activation maps

Mona Schumacher, Andreas Genz, Mattias Heinrich

Proceedings Volume 11313, 1131314 (2020) https://doi.org/10.1117/12.2549353

KEYWORDS: Pancreas, Image segmentation, Machine learning, Image classification, Neural networks, Image processing, Convolutional neural networks, Medicine

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SPIE Journal Paper | 4 April 2018 Open Access

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

Bartłomiej Papież, James Franklin, Mattias Heinrich, Fergus Gleeson, Michael Brady, Julia Schnabel

JMI, Vol. 5, Issue 02, 024001, (April 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.024001

KEYWORDS: Image filtering, Liver, Image registration, Lung, Gaussian filters, Computed tomography, Image segmentation, Motion estimation, Medical imaging, Diffusion

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Proceedings Article | 2 March 2018 Paper

Orientation regression in hand radiographs: a transfer learning approach

Ivo M. Baltruschat, Axel Saalbach, Mattias Heinrich, Hannes Nickisch, Sascha Jockel

Proceedings Volume 10574, 105741W (2018) https://doi.org/10.1117/12.2291620

KEYWORDS: X-rays, X-ray imaging, Radiography, Medical imaging, Neural networks, Automatic alignment, X-ray detectors, Chest, Digital x-ray imaging, Digital imaging

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Proceedings Article | 2 March 2018 Presentation + Paper

Nearest neighbor 3D segmentation with context features

Evelin Hristova, Heinrich Schulz, Tom Brosch, Mattias Heinrich, Hannes Nickisch

Proceedings Volume 10574, 105740M (2018) https://doi.org/10.1117/12.2292181

KEYWORDS: Image segmentation, Kidney, Binary data, Feature extraction, Computed tomography, Magnetic resonance imaging, Liver, Bladder, Prostate, Spleen

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Showing 5 of 15 publications

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