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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22829 |
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| _version_ | 1866910900778172416 |
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| author | Lin, Zhen Yuan, Hongyu Barcus, Richard Lyu, Qing Chakravarty, Sucheta Lipford, Megan E. Shively, Carol A. Craft, Suzanne Kawas, Mohammad Kim, Jeongchul Whitlow, Christopher T. |
| author_facet | Lin, Zhen Yuan, Hongyu Barcus, Richard Lyu, Qing Chakravarty, Sucheta Lipford, Megan E. Shively, Carol A. Craft, Suzanne Kawas, Mohammad Kim, Jeongchul Whitlow, Christopher T. |
| contents | Non-human primates (NHPs) serve as critical models for understanding human brain function and neurological disorders due to their close evolutionary relationship with humans. Accurate brain tissue segmentation in NHPs is critical for understanding neurological disorders, but challenging due to the scarcity of annotated NHP brain MRI datasets, the small size of the NHP brain, the limited resolution of available imaging data and the anatomical differences between human and NHP brains. To address these challenges, we propose a novel approach utilizing STU-Net with transfer learning to leverage knowledge transferred from human brain MRI data to enhance segmentation accuracy in the NHP brain MRI, particularly when training data is limited. The combination of STU-Net and transfer learning effectively delineates complex tissue boundaries and captures fine anatomical details specific to NHP brains. Notably, our method demonstrated improvement in segmenting small subcortical structures such as putamen and thalamus that are challenging to resolve with limited spatial resolution and tissue contrast, and achieved DSC of over 0.88, IoU over 0.8 and HD95 under 7. This study introduces a robust method for multi-class brain tissue segmentation in NHPs, potentially accelerating research in evolutionary neuroscience and preclinical studies of neurological disorders relevant to human health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach Lin, Zhen Yuan, Hongyu Barcus, Richard Lyu, Qing Chakravarty, Sucheta Lipford, Megan E. Shively, Carol A. Craft, Suzanne Kawas, Mohammad Kim, Jeongchul Whitlow, Christopher T. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Non-human primates (NHPs) serve as critical models for understanding human brain function and neurological disorders due to their close evolutionary relationship with humans. Accurate brain tissue segmentation in NHPs is critical for understanding neurological disorders, but challenging due to the scarcity of annotated NHP brain MRI datasets, the small size of the NHP brain, the limited resolution of available imaging data and the anatomical differences between human and NHP brains. To address these challenges, we propose a novel approach utilizing STU-Net with transfer learning to leverage knowledge transferred from human brain MRI data to enhance segmentation accuracy in the NHP brain MRI, particularly when training data is limited. The combination of STU-Net and transfer learning effectively delineates complex tissue boundaries and captures fine anatomical details specific to NHP brains. Notably, our method demonstrated improvement in segmenting small subcortical structures such as putamen and thalamus that are challenging to resolve with limited spatial resolution and tissue contrast, and achieved DSC of over 0.88, IoU over 0.8 and HD95 under 7. This study introduces a robust method for multi-class brain tissue segmentation in NHPs, potentially accelerating research in evolutionary neuroscience and preclinical studies of neurological disorders relevant to human health. |
| title | Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.22829 |