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3. TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

Author

D'Antonoli, Tugba Akinci, Berger, Lucas K., Indrakanti, Ashraya K., Vishwanathan, Nathan, Weiß, Jakob, Jung, Matthias, Berkarda, Zeynep, Rau, Alexander, Reisert, Marco, Küstner, Thomas, Walter, Alexandra, Merkle, Elmar M., Boll, Daniel, Breit, Hanns-Christian, Nicoli, Andrew Phillip, Segeroth, Martin, Cyriac, Joshy, Yang, Shan, and Wasserthal, Jakob

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

Since the introduction of TotalSegmentator CT, there is demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. In this retrospective study, a nnU-Net model (TotalSegmentator) was trained on MRI and CT examinations to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning and opportunistic screening. Examinations were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist reference standard segmentations to evaluate model performance on an internal test set, two external test sets and against two publicly available models, and TotalSegmentator CT. The model was applied to an internal dataset containing abdominal MRIs to investigate age-dependent volume changes. A total of 1143 examinations (616 MRIs, 527 CTs) (median age 61 years, IQR 50-72) were split into training (n=1088, CT and MRI) and an internal test set (n=55; only MRI), two external test sets (AMOS, n=20; CHAOS, n=20; only MRI), and an internal aging-study dataset of 8672 abdominal MRIs (median age 59 years, IQR 45-70) were included. The model showed a Dice Score of 0.839 on the internal test set and outperformed two other models (Dice Score, 0.862 versus 0.759; and 0.838 versus 0.560; p<.001 for both). The proposed open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures, extending the capabilities of TotalSegmentator to MRIs of any sequence. The ready-to-use online tool is available at https://totalsegmentator.com, the model at https://github.com/wasserth/TotalSegmentator, and the dataset at https://zenodo.org/records/14710732., Comment: Published in Radiology

Published
2024
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10. Cryo-EM structures of the human Elongator complex at work

Author

Abbassi, Nour-el-Hana, Jaciuk, Marcin, Scherf, David, Böhnert, Pauline, Rau, Alexander, Hammermeister, Alexander, Rawski, Michał, Indyka, Paulina, Wazny, Grzegorz, Chramiec-Głąbik, Andrzej, Dobosz, Dominika, Skupien-Rabian, Bozena, Jankowska, Urszula, Rappsilber, Juri, Schaffrath, Raffael, Lin, Ting-Yu, and Glatt, Sebastian

Published
2024
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38. Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn).

Author

Elsheikh, Samer, Elbaz, Ahmed, Rau, Alexander, Demerath, Theo, Kellner, Elias, Watzlawick, Ralf, Würtemberger, Urs, Urbach, Horst, and Reisert, Marco

Subjects
CEREBRAL hemorrhage, RECEIVER operating characteristic curves, ARTIFICIAL intelligence, INTRACLASS correlation, MINIMALLY invasive procedures
Abstract

Background and purpose: External drainage represents a well-established treatment option for acute intracerebral hemorrhage. The current standard of practice includes post-operative computer tomography imaging, which is subjectively evaluated. The implementation of an objective, automated evaluation of postoperative studies may enhance diagnostic accuracy and facilitate the scaling of research projects. The objective is to develop and validate a fully automated pipeline for intracerebral hemorrhage and drain detection, quantification of intracerebral hemorrhage coverage, and detection of malpositioned drains. Materials and methods: In this retrospective study, we selected patients (n = 68) suffering from supratentorial intracerebral hemorrhage treated by minimally invasive surgery, from years 2010–2018. These were divided into training (n = 21), validation (n = 3) and testing (n = 44) datasets. Mean age (SD) was 70 (±13.56) years, 32 female. Intracerebral hemorrhage and drains were automatically segmented using a previously published artificial intelligence-based approach. From this, we calculated coverage profiles of the correctly detected drains to quantify the drains' coverage by the intracerebral hemorrhage and classify malpositioning. We used accuracy measures to assess detection and classification results and intraclass correlation coefficient to assess the quantification of the drain coverage by the intracerebral hemorrhage. Results: In the test dataset, the pipeline showed a drain detection accuracy of 0.97 (95% CI: 0.92 to 0.99), an agreement between predicted and ground truth coverage profiles of 0.86 (95% CI: 0.85 to 0.87) and a drain position classification accuracy of 0.88 (95% CI: 0.77 to 0.95) resulting in area under the receiver operating characteristic curve of 0.92 (95% CI: 0.85 to 0.99). Conclusion: We developed and statistically validated an automated pipeline for evaluating computed tomography scans after minimally invasive surgery for intracerebral hemorrhage. The algorithm reliably detects drains, quantifies drain coverage by the hemorrhage, and uses machine learning to detect malpositioned drains. This pipeline has the potential to impact the daily clinical workload, as well as to facilitate the scaling of data collection for future research into intracerebral hemorrhage and other diseases. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Inflammation‐Triggered Enlargement of Choroid Plexus in Subacute COVID‐19 Patients with Neurological Symptoms.

Author

Rau, Alexander, Gonzalez‐Escamilla, Gabriel, Schroeter, Nils, Othman, Ahmed, Dressing, Andrea, Weiller, Cornelius, Urbach, Horst, Reisert, Marco, Groppa, Sergiu, and Hosp, Jonas A.

Subjects
*SARS-CoV-2, *CHOROID plexus, *MONTREAL Cognitive Assessment, *COVID-19, *CENTRAL nervous system
Abstract

Objective: To investigate whether choroid plexus volumes in subacute coronavirus disease 2019 (COVID‐19) patients with neurological symptoms could indicate inflammatory activation or barrier dysfunction and assess their association with clinical data. Methods: Choroid plexus volumes were measured in 28 subacute COVID‐19 patients via cerebral magnetic resonance imaging (MRI), compared with those in infection‐triggered non‐COVID‐19 encephalopathy patients (n = 25), asymptomatic individuals after COVID‐19 (n = 21), and healthy controls (n = 21). Associations with inflammatory serum markers (peak counts of leukocytes, C‐reactive protein [CRP], interleukin 6), an MRI‐based marker of barrier dysfunction (CSF volume fraction [V‐CSF]), and clinical parameters like olfactory performance and cognitive scores (Montreal Cognitive Assessment) were investigated. Results: COVID‐19 patients showed significantly larger choroid plexus volumes than control groups (p < 0.001, η2 = 0.172). These volumes correlated significantly with peak leukocyte levels (p = 0.001, Pearson's r = 0.621) and V‐CSF (p = 0.009, Spearman's rho = 0.534), but neither with CRP nor interleukin 6. No significant correlations were found with clinical parameters. Interpretation: In patients with subacute COVID‐19, choroid plexus volume is a marker of central nervous system inflammation and barrier dysfunction in the presence of neurologic symptoms. The absence of plexus enlargement in infection‐triggered non‐COVID‐19 encephalopathy suggests a specific severe acute respiratory syndrome coronavirus 2 effect. This study also documents an increase in choroid plexus volume for the first time as a parainfectious event. ANN NEUROL 2024;96:715–725 [ABSTRACT FROM AUTHOR]

Published
2024
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42. Pre-arrayed Pan-AAV Peptide Display Libraries for Rapid Single-Round Screening

Author

Börner, Kathleen, Kienle, Eike, Huang, Lin-Ya, Weinmann, Jonas, Sacher, Anna, Bayer, Philipp, Stüllein, Christian, Fakhiri, Julia, Zimmermann, Laura, Westhaus, Adrian, Beneke, Jürgen, Beil, Nina, Wiedtke, Ellen, Schmelas, Carolin, Miltner, Dominik, Rau, Alexander, Erfle, Holger, Kräusslich, Hans-Georg, Müller, Martin, Agbandje-McKenna, Mavis, and Grimm, Dirk

Published
2020
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43. Advanced diffusion imaging reveals microstructural characteristics of primary CNS lymphoma, allowing differentiation from glioblastoma

Author

Würtemberger, Urs, primary, Diebold, Martin, additional, Rau, Alexander, additional, Akgün, Veysel, additional, Becker, Lucas, additional, Beck, Jürgen, additional, Reinacher, Peter C, additional, Taschner, Christian A, additional, Reisert, Marco, additional, Fehrenbacher, Luca, additional, Erny, Daniel, additional, Scherer, Florian, additional, Hohenhaus, Marc, additional, Urbach, Horst, additional, and Demerath, Theo, additional

Published
2024
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45. TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images

Author

D'Antonoli, Tugba Akinci, Berger, Lucas K., Indrakanti, Ashraya K., Vishwanathan, Nathan, Weiß, Jakob, Jung, Matthias, Berkarda, Zeynep, Rau, Alexander, Reisert, Marco, Küstner, Thomas, Walter, Alexandra, Merkle, Elmar M., Segeroth, Martin, Cyriac, Joshy, Yang, Shan, Wasserthal, Jakob, D'Antonoli, Tugba Akinci, Berger, Lucas K., Indrakanti, Ashraya K., Vishwanathan, Nathan, Weiß, Jakob, Jung, Matthias, Berkarda, Zeynep, Rau, Alexander, Reisert, Marco, Küstner, Thomas, Walter, Alexandra, Merkle, Elmar M., Segeroth, Martin, Cyriac, Joshy, Yang, Shan, and Wasserthal, Jakob

Abstract

Purpose: To develop an open-source and easy-to-use segmentation model that can automatically and robustly segment most major anatomical structures in MR images independently of the MR sequence. Materials and Methods: In this study we extended the capabilities of TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3 tissue types) relevant for use cases such as organ volumetry, disease characterization, and surgical planning. The MR and CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, contrasts, echo times, repetition times, field strengths, slice thicknesses and sites). We trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed two other publicly available segmentation models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542; p<0.001). On the CT image test set of the original TotalSegmentator paper it almost matches the performance of the original TotalSegmentator (Dice score, 0.960 versus 0.970; p<0.001). Conclusion: Our proposed model extends the capabilities of TotalSegmentator to MR images. The annotated dataset (https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.

Published
2024

47. Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach

Author

Michel, Lea J., primary, Rospleszcz, Susanne, additional, Reisert, Marco, additional, Rau, Alexander, additional, Nattenmueller, Johanna, additional, Rathmann, Wolfgang, additional, Schlett, Christopher. L., additional, Peters, Annette, additional, Bamberg, Fabian, additional, and Weiss, Jakob, additional

Published
2024
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48. A content-aware chatbot based on GPT 4 provides trustworthy recommendations for Cone-Beam CT guidelines in dental imaging

Author

Russe, Maximilian Frederik, primary, Rau, Alexander, additional, Ermer, Michael Andreas, additional, Rothweiler, René, additional, Wenger, Sina, additional, Klöble, Klara, additional, Schulze, Ralf K W, additional, Bamberg, Fabian, additional, Schmelzeisen, Rainer, additional, Reisert, Marco, additional, and Semper-Hogg, Wiebke, additional

Published
2024
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