A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone
Abstract Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning architecture designed for automatically segment...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92954-1 |
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| author | Amine Lagzouli Peter Pivonka David M. L. Cooper Vittorio Sansalone Alice Othmani |
| author_facet | Amine Lagzouli Peter Pivonka David M. L. Cooper Vittorio Sansalone Alice Othmani |
| author_sort | Amine Lagzouli |
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| description | Abstract Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning architecture designed for automatically segmenting the cortical and trabecular compartments in 3D µCT scans of mouse tibiae. DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. DBAHNet demonstrated excellent performance, achieving high accuracy, particularly in challenging scenarios with significantly altered bone morphology. The model’s robustness and generalization capabilities were rigorously tested under diverse and unseen conditions, confirming its effectiveness in the automated segmentation of high-resolution µCT mouse tibia scans. Our findings highlight DBAHNet’s potential to provide reliable and accurate 3D µCT mouse tibia segmentation, thereby enhancing and accelerating preclinical bone studies in drug development. The model and code are available at https://github.com/bigfahma/DBAHNet . |
| format | Article |
| id | doaj-art-e346a3ff3a6b4205b11bf9cbf59ce57c |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e346a3ff3a6b4205b11bf9cbf59ce57c2025-08-20T02:56:19ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-92954-1A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse boneAmine Lagzouli0Peter Pivonka1David M. L. Cooper2Vittorio Sansalone3Alice Othmani4School of Mechanical, Medical, and Process Engineering, Queensland University of TechnologySchool of Mechanical, Medical, and Process Engineering, Queensland University of TechnologyDepartment of Anatomy, Physiology, and Pharmacology, University of SaskatchewanUniv Paris Est Créteil, Univ Gustave Eiffel, CNRS, UMR 8208, MSMELISSI, Université Paris-Est Creteil (UPEC)Abstract Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning architecture designed for automatically segmenting the cortical and trabecular compartments in 3D µCT scans of mouse tibiae. DBAHNet’s hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. DBAHNet demonstrated excellent performance, achieving high accuracy, particularly in challenging scenarios with significantly altered bone morphology. The model’s robustness and generalization capabilities were rigorously tested under diverse and unseen conditions, confirming its effectiveness in the automated segmentation of high-resolution µCT mouse tibia scans. Our findings highlight DBAHNet’s potential to provide reliable and accurate 3D µCT mouse tibia segmentation, thereby enhancing and accelerating preclinical bone studies in drug development. The model and code are available at https://github.com/bigfahma/DBAHNet .https://doi.org/10.1038/s41598-025-92954-13D image segmentationDeep learningMicrocomputed tomography µCTHigh-resolutionMouse tibiaRobust model |
| spellingShingle | Amine Lagzouli Peter Pivonka David M. L. Cooper Vittorio Sansalone Alice Othmani A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone Scientific Reports 3D image segmentation Deep learning Microcomputed tomography µCT High-resolution Mouse tibia Robust model |
| title | A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone |
| title_full | A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone |
| title_fullStr | A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone |
| title_full_unstemmed | A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone |
| title_short | A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone |
| title_sort | robust deep learning approach for segmenting cortical and trabecular bone from 3d high resolution µct scans of mouse bone |
| topic | 3D image segmentation Deep learning Microcomputed tomography µCT High-resolution Mouse tibia Robust model |
| url | https://doi.org/10.1038/s41598-025-92954-1 |
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