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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2405.06463 |
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| _version_ | 1866915433614934016 |
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| author | Häntze, Hartmut Xu, Lina Mertens, Christian J. Dorfner, Felix J. Donle, Leonhard Busch, Felix Kader, Avan Ziegelmayer, Sebastian Bayerl, Nadine Navab, Nassir Rueckert, Daniel Schnabel, Julia Aerts, Hugo JWL Truhn, Daniel Bamberg, Fabian Weiß, Jakob Schlett, Christopher L. Ringhof, Steffen Niendorf, Thoralf Pischon, Tobias Kauczor, Hans-Ulrich Nonnenmacher, Tobias Kröncke, Thomas Völzke, Henry Schulz-Menger, Jeanette Maier-Hein, Klaus Prokop, Mathias van Ginneken, Bram Hering, Alessa Makowski, Marcus R. Adams, Lisa C. Bressem, Keno K. |
| author_facet | Häntze, Hartmut Xu, Lina Mertens, Christian J. Dorfner, Felix J. Donle, Leonhard Busch, Felix Kader, Avan Ziegelmayer, Sebastian Bayerl, Nadine Navab, Nassir Rueckert, Daniel Schnabel, Julia Aerts, Hugo JWL Truhn, Daniel Bamberg, Fabian Weiß, Jakob Schlett, Christopher L. Ringhof, Steffen Niendorf, Thoralf Pischon, Tobias Kauczor, Hans-Ulrich Nonnenmacher, Tobias Kröncke, Thomas Völzke, Henry Schulz-Menger, Jeanette Maier-Hein, Klaus Prokop, Mathias van Ginneken, Bram Hering, Alessa Makowski, Marcus R. Adams, Lisa C. Bressem, Keno K. |
| contents | Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans.
Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT scans from the TotalSegmentator dataset. A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures. The annotation process began with a model based on transfer learning between CT and MR, which was iteratively refined based on manual corrections to predicted segmentations. The model's performance was evaluated on MRI examinations obtained from the German National Cohort (NAKO) study (n=900) from the AMOS22 dataset (n=60) and from the TotalSegmentator-MRI test data (n=29). The Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used to assess segmentation quality, stratified by organ and scan type. The model and its weights will be open-sourced.
Results: MRSegmentator demonstrated high accuracy for well-defined organs (lungs: DSC 0.96, heart: DSC 0.94) and organs with anatomic variability (liver: DSC 0.96, kidneys: DSC 0.95). Smaller structures showed lower accuracy (portal/splenic veins: DSC 0.64, adrenal glands: DSC 0.69). On external validation using NAKO data, mean DSC ranged from 0.85 $\pm$ 0.08 for T2-HASTE to 0.91 $\pm$ 0.05 for in-phase sequences. The model generalized well to CT, achieving mean DSC of 0.84 $\pm$ 0.11 on AMOS CT data.
Conclusion: MRSegmentator accurately segments 40 anatomical structures in MRI across diverse datasets and imaging protocols, with additional generalizability to CT images. This open-source model will provide a valuable tool for automated multi-organ segmentation in medical imaging research. It can be downloaded from https://github.com/hhaentze/MRSegmentator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT Häntze, Hartmut Xu, Lina Mertens, Christian J. Dorfner, Felix J. Donle, Leonhard Busch, Felix Kader, Avan Ziegelmayer, Sebastian Bayerl, Nadine Navab, Nassir Rueckert, Daniel Schnabel, Julia Aerts, Hugo JWL Truhn, Daniel Bamberg, Fabian Weiß, Jakob Schlett, Christopher L. Ringhof, Steffen Niendorf, Thoralf Pischon, Tobias Kauczor, Hans-Ulrich Nonnenmacher, Tobias Kröncke, Thomas Völzke, Henry Schulz-Menger, Jeanette Maier-Hein, Klaus Prokop, Mathias van Ginneken, Bram Hering, Alessa Makowski, Marcus R. Adams, Lisa C. Bressem, Keno K. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning J.3 Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT scans from the TotalSegmentator dataset. A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures. The annotation process began with a model based on transfer learning between CT and MR, which was iteratively refined based on manual corrections to predicted segmentations. The model's performance was evaluated on MRI examinations obtained from the German National Cohort (NAKO) study (n=900) from the AMOS22 dataset (n=60) and from the TotalSegmentator-MRI test data (n=29). The Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used to assess segmentation quality, stratified by organ and scan type. The model and its weights will be open-sourced. Results: MRSegmentator demonstrated high accuracy for well-defined organs (lungs: DSC 0.96, heart: DSC 0.94) and organs with anatomic variability (liver: DSC 0.96, kidneys: DSC 0.95). Smaller structures showed lower accuracy (portal/splenic veins: DSC 0.64, adrenal glands: DSC 0.69). On external validation using NAKO data, mean DSC ranged from 0.85 $\pm$ 0.08 for T2-HASTE to 0.91 $\pm$ 0.05 for in-phase sequences. The model generalized well to CT, achieving mean DSC of 0.84 $\pm$ 0.11 on AMOS CT data. Conclusion: MRSegmentator accurately segments 40 anatomical structures in MRI across diverse datasets and imaging protocols, with additional generalizability to CT images. This open-source model will provide a valuable tool for automated multi-organ segmentation in medical imaging research. It can be downloaded from https://github.com/hhaentze/MRSegmentator. |
| title | MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning J.3 |
| url | https://arxiv.org/abs/2405.06463 |