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2. Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

Author

Weissmann, Thomas, Huang, Yixing, Fischer, Stefan, Roesch, Johannes, Mansoorian, Sina, Gaona, Horacio Ayala, Gostian, Antoniu-Oreste, Hecht, Markus, Lettmaier, Sebastian, Deloch, Lisa, Frey, Benjamin, Gaipl, Udo S., Distel, Luitpold V., Maier, Andreas, Iro, Heinrich, Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, and Putz, Florian

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

Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n=20). In a completely blinded evaluation, 3 clinical experts rated the quality of DL autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for DL segmentations and expert-created contours were not significantly different. DL segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6,p=0.185) and DL segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6,p=0.167) than manually drawn contours. DL segmentations with CT slice plane adjustment were rated significantly better than DL contours without slice plane adjustment (81.0 vs. 77.2,p=0.004). Geometric accuracy of DL segmentations was not different from intraobserver variability (mean, 0.76 vs. 0.77, p=0.307). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting., Comment: 14 pages, 6 figures, published in Frontiers in Oncology

Published
2022
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5. Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design

Author

Weissmann, Thomas, primary, Mansoorian, Sina, additional, May, Matthias Stefan, additional, Lettmaier, Sebastian, additional, Höfler, Daniel, additional, Deloch, Lisa, additional, Speer, Stefan, additional, Balk, Matthias, additional, Frey, Benjamin, additional, Gaipl, Udo S., additional, Bert, Christoph, additional, Distel, Luitpold Valentin, additional, Walter, Franziska, additional, Belka, Claus, additional, Semrau, Sabine, additional, Iro, Heinrich, additional, Fietkau, Rainer, additional, Huang, Yixing, additional, and Putz, Florian, additional

Published
2023
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6. Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

Author

Weissmann, Thomas, primary, Huang, Yixing, additional, Fischer, Stefan, additional, Roesch, Johannes, additional, Mansoorian, Sina, additional, Ayala Gaona, Horacio, additional, Gostian, Antoniu-Oreste, additional, Hecht, Markus, additional, Lettmaier, Sebastian, additional, Deloch, Lisa, additional, Frey, Benjamin, additional, Gaipl, Udo S., additional, Distel, Luitpold Valentin, additional, Maier, Andreas, additional, Iro, Heinrich, additional, Semrau, Sabine, additional, Bert, Christoph, additional, Fietkau, Rainer, additional, and Putz, Florian, additional

Published
2023
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7. Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

Author

Weissmann, Thomas, Huang, Yixing, Fischer, Stefan, Roesch, Johannes, Mansoorian, Sina, Gaona, Horacio Ayala, Gostian, Antoniu-Oreste, Hecht, Markus, Lettmaier, Sebastian, Deloch, Lisa, Frey, Benjamin, Gaipl, Udo S., Distel, Luitpold V., Maier, Andreas, Iro, Heinrich, Semrau, Sabine, Bert, Christoph, Fietkau, Rainer, and Putz, Florian

Subjects
FOS: Computer and information sciences, Cancer Research, Oncology, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, ddc:610, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Background: Deep learning (DL)-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n=20). In a completely blinded evaluation, 3 clinical experts rated the quality of DL autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for DL segmentations and expert-created contours were not significantly different. DL segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6,p=0.185) and DL segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6,p=0.167) than manually drawn contours. DL segmentations with CT slice plane adjustment were rated significantly better than DL contours without slice plane adjustment (81.0 vs. 77.2,p=0.004). Geometric accuracy of DL segmentations was not different from intraobserver variability (mean, 0.76 vs. 0.77, p=0.307). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting., 14 pages, 6 figures, published in Frontiers in Oncology

Published
2023

8. Transient Enlargement in Meningiomas Treated with Stereotactic Radiotherapy

Author

Maksoud, Ziad, primary, Schmidt, Manuel Alexander, additional, Huang, Yixing, additional, Rutzner, Sandra, additional, Mansoorian, Sina, additional, Weissmann, Thomas, additional, Bert, Christoph, additional, Distel, Luitpold, additional, Semrau, Sabine, additional, Lettmaier, Sebastian, additional, Eyüpoglu, Ilker, additional, Fietkau, Rainer, additional, and Putz, Florian, additional

Published
2022
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9. The Distribution of Pelvic Nodal Metastases in Prostate Cancer Reveals Potential to Advance and Personalize Pelvic Radiotherapy

Author

Filimonova, Irina, primary, Schmidt, Daniela, additional, Mansoorian, Sina, additional, Weissmann, Thomas, additional, Siavooshhaghighi, Hadi, additional, Cavallaro, Alexander, additional, Kuwert, Torsten, additional, Bert, Christoph, additional, Frey, Benjamin, additional, Distel, Luitpold Valentin, additional, Lettmaier, Sebastian, additional, Fietkau, Rainer, additional, and Putz, Florian, additional

Published
2021
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10. Implementation of a dedicated 1.5 T MR scanner for radiotherapy treatment planning featuring a novel high-channel coil setup for brain imaging in treatment position

Author

Mengling, Veit, primary, Bert, Christoph, additional, Perrin, Rosalind, additional, Masitho, Siti, additional, Weissmann, Thomas, additional, Mansoorian, Sina, additional, Siavooshhaghighi, Hadi, additional, Janka, Rolf, additional, Doussin, Sylvain, additional, Habatsch, Melanie, additional, Fietkau, Rainer, additional, and Putz, Florian, additional

Published
2020
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