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Article: Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection.

Schlaeger, Sarah / Drummer, Katharina / Husseini, Malek El / Kofler, Florian / Sollmann, Nico / Schramm, Severin / Zimmer, Claus / Kirschke, Jan S / Wiestler, Benedikt

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 5

Abstract: 1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently ... ...

Abstract	(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time. Therefore, by simulating the radiological workflow with a heterogenous dataset, this study's purpose was to evaluate the diagnostic value of additional synthetic, GAN-based T2-w fs images in the clinical routine. (2) Methods: 174 patients with MRI of the spine were retrospectively identified. A GAN was trained to synthesize T2-w fs images from T1-w, and non-fs T2-w images of 73 patients scanned in our institution. Subsequently, the GAN was used to create synthetic T2-w fs images for the previously unseen 101 patients from multiple institutions. In this test dataset, the additional diagnostic value of synthetic T2-w fs images was assessed in six pathologies by two neuroradiologists. Pathologies were first graded on T1-w and non-fs T2-w images only, then synthetic T2-w fs images were added, and pathologies were graded again. Evaluation of the additional diagnostic value of the synthetic protocol was performed by calculation of Cohen's ĸ and accuracy in comparison to a ground truth (GT) grading based on real T2-w fs images, pre- or follow-up scans, other imaging modalities, and clinical information. (3) Results: The addition of the synthetic T2-w fs to the imaging protocol led to a more precise grading of abnormalities than when grading was based on T1-w and non-fs T2-w images only (mean ĸ GT versus synthetic protocol = 0.65; mean ĸ GT versus T1/T2 = 0.56;
Language	English
Publishing date	2023-03-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13050974
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Article ; Online: Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset.

Schlaeger, Sarah / Drummer, Katharina / El Husseini, Malek / Kofler, Florian / Sollmann, Nico / Schramm, Severin / Zimmer, Claus / Wiestler, Benedikt / Kirschke, Jan S

European radiology

2023 Volume 33, Issue 8, Page(s) 5882–5893

Abstract: Objectives: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w ... ...

Abstract	Objectives: T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset. Methods: A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists. Results: aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies. Discussion: The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols. Key points: • Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine. • The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images. • The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.
MeSH term(s)	Humans ; Spine/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Radionuclide Imaging
Language	English
Publishing date	2023-03-16
Publishing country	Germany
Document type	Multicenter Study ; Journal Article
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-023-09512-4
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Book ; Online ; Thesis: The Hitchhiker's Guide to Machine Learning for Biomedical Image Analysis

Kofler, Florian Verfasser] / [Menze, Bjoern Holger [Akademischer Betreuer] / Menze, Bjoern Holger [Gutachter] / Wiest, Roland [Gutachter]

2023

Author's details	Florian Kofler ; Gutachter: Björn Menze, Roland Wiest ; Betreuer: Björn Menze
Keywords	Naturwissenschaften ; Science
Subject code	sg500
Language	English
Publisher	Universitätsbibliothek der TU München
Publishing place	München
Document type	Book ; Online ; Thesis
Database	Digital theses on the web

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Article ; Online: A Learnable Prior Improves Inverse Tumor Growth Modeling.

Weidner, Jonas / Ezhov, Ivan / Balcerak, Michal / Metz, Marie-Christin / Litvinov, Sergey / Kaltenbach, Sebastian / Feiner, Leonhard / Lux, Laurin / Kofler, Florian / Lipkova, Jana / Latz, Jonas / Rueckert, Daniel / Menze, Bjoern / Wiestler, Benedikt

ArXiv

2024

Abstract: Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a ... ...

Abstract	Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95.
Language	English
Publishing date	2024-03-07
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Virtual reality-empowered deep-learning analysis of brain cells.

Kaltenecker, Doris / Al-Maskari, Rami / Negwer, Moritz / Hoeher, Luciano / Kofler, Florian / Zhao, Shan / Todorov, Mihail / Rong, Zhouyi / Paetzold, Johannes Christian / Wiestler, Benedikt / Piraud, Marie / Rueckert, Daniel / Geppert, Julia / Morigny, Pauline / Rohm, Maria / Menze, Bjoern H / Herzig, Stephan / Berriel Diaz, Mauricio / Ertürk, Ali

Nature methods

2024

Abstract: Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c- ... ...

Abstract	Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos
Language	English
Publishing date	2024-04-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-024-02245-2
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Book ; Online: Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Ezhov, Ivan / Giannoni, Luca / Shit, Suprosanna / Lange, Frederic / Kofler, Florian / Menze, Bjoern / Tachtsidis, Ilias / Rueckert, Daniel

2023

Abstract: Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its ... ...

Abstract	Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery.
Keywords	Quantitative Biology - Quantitative Methods ; Physics - Data Analysis ; Statistics and Probability ; Physics - Optics
Subject code	571
Publishing date	2023-01-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Whole-body cellular mapping in mouse using standard IgG antibodies.

Mai, Hongcheng / Luo, Jie / Hoeher, Luciano / Al-Maskari, Rami / Horvath, Izabela / Chen, Ying / Kofler, Florian / Piraud, Marie / Paetzold, Johannes C / Modamio, Jennifer / Todorov, Mihail / Elsner, Markus / Hellal, Farida / Ertürk, Ali

Nature biotechnology

2023 Volume 42, Issue 4, Page(s) 617–627

Abstract: Whole-body imaging techniques play a vital role in exploring the interplay of physiological systems in maintaining health and driving disease. We introduce wildDISCO, a new approach for whole-body immunolabeling, optical clearing and imaging in mice, ... ...

Abstract	Whole-body imaging techniques play a vital role in exploring the interplay of physiological systems in maintaining health and driving disease. We introduce wildDISCO, a new approach for whole-body immunolabeling, optical clearing and imaging in mice, circumventing the need for transgenic reporter animals or nanobody labeling and so overcoming existing technical limitations. We identified heptakis(2,6-di-O-methyl)-β-cyclodextrin as a potent enhancer of cholesterol extraction and membrane permeabilization, enabling deep, homogeneous penetration of standard antibodies without aggregation. WildDISCO facilitates imaging of peripheral nervous systems, lymphatic vessels and immune cells in whole mice at cellular resolution by labeling diverse endogenous proteins. Additionally, we examined rare proliferating cells and the effects of biological perturbations, as demonstrated in germ-free mice. We applied wildDISCO to map tertiary lymphoid structures in the context of breast cancer, considering both primary tumor and metastases throughout the mouse body. An atlas of high-resolution images showcasing mouse nervous, lymphatic and vascular systems is accessible at http://discotechnologies.org/wildDISCO/atlas/index.php .
MeSH term(s)	Mice ; Animals ; Immunoglobulin G ; Imaging, Three-Dimensional
Chemical Substances	Immunoglobulin G
Language	English
Publishing date	2023-07-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-023-01846-0
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Article ; Online: Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans.

Thomas, Marie Franziska / Kofler, Florian / Grundl, Lioba / Finck, Tom / Li, Hongwei / Zimmer, Claus / Menze, Björn / Wiestler, Benedikt

Investigative radiology

2021 Volume 57, Issue 3, Page(s) 187–193

Abstract: Objectives: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop ...

Abstract	Objectives: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. Materials and methods: Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to synthesize either missing FLAIR, T2-weighted, T1-weighted (T1w), or contrast-enhanced T1w images from available sequences, using a novel tumor-targeting loss to improve synthesis of tumor areas. We validated performance in a test set from both the REMBRANDT repository and our local institution (n = 68 WHO II-IV gliomas), using qualitative image appearance metrics, but also segmentation performance with state-of-the-art segmentation models. Segmentation of synthetic images was compared with 2 commonly used strategies for handling missing input data, entering a blank mask or copying an existing sequence. Results: Across tumor areas and missing sequences, synthetic images generally outperformed both conventional approaches, in particular when FLAIR was missing. Here, for edema and whole tumor segmentation, we improved the Dice score, a common metric for evaluation of segmentation performance, by 12% and 11%, respectively, over the best conventional method. No method was able to reliably replace missing contrast-enhanced T1w images. Discussion: Replacing missing nonenhanced magnetic resonance sequences via synthetic images significantly improves segmentation quality over most conventional approaches. This model is freely available and facilitates more widespread use of automated segmentation in routine clinical use, where missing sequences are common.
MeSH term(s)	Artificial Intelligence ; Brain Neoplasms/diagnostic imaging ; Glioma/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2021-10-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80345-5
ISSN	1536-0210 ; 0020-9996
ISSN (online)	1536-0210
ISSN	0020-9996
DOI	10.1097/RLI.0000000000000828
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Article ; Online: A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.

IEEE transactions on pattern analysis and machine intelligence

2024 Volume 46, Issue 5, Page(s) 3784–3795

Abstract: Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine ... ...

Abstract	Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.
MeSH term(s)	Artificial Intelligence ; Algorithms ; Magnetic Resonance Imaging ; Fetus/diagnostic imaging ; Brain/diagnostic imaging
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3346330
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Article ; Online: LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

NeuroImage. Clinical

2024 Volume 42, Page(s) 103611

Abstract: Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion ... ...

Abstract	Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm
Language	English
Publishing date	2024-04-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2701571-3
ISSN	2213-1582 ; 2213-1582
ISSN (online)	2213-1582
ISSN	2213-1582
DOI	10.1016/j.nicl.2024.103611
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