3D Medical Imaging Segmentation on Non-Contrast CT
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917951456673792 |
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| author | Gang, Canxuan Peng, Yuhan |
| author_facet | Gang, Canxuan Peng, Yuhan |
| contents | This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. The study reviews representative methods, including convolutional-based and CNN-Transformer hybrid approaches, discussing their contributions, advantages, and limitations. The nnUNet stands out as the state-of-the-art method across various segmentation tasks. The report explores the relationship between the proposed method and existing approaches, emphasizing the role of global context modeling in semantic labeling and mask generation. Future directions include addressing the long-tail problem, utilizing pre-trained models for medical imaging, and exploring self-supervised or contrastive pre-training techniques. This report offers insights into non-contrast CT image segmentation and potential advancements in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Medical Imaging Segmentation on Non-Contrast CT Gang, Canxuan Peng, Yuhan Image and Video Processing Computer Vision and Pattern Recognition This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. The study reviews representative methods, including convolutional-based and CNN-Transformer hybrid approaches, discussing their contributions, advantages, and limitations. The nnUNet stands out as the state-of-the-art method across various segmentation tasks. The report explores the relationship between the proposed method and existing approaches, emphasizing the role of global context modeling in semantic labeling and mask generation. Future directions include addressing the long-tail problem, utilizing pre-trained models for medical imaging, and exploring self-supervised or contrastive pre-training techniques. This report offers insights into non-contrast CT image segmentation and potential advancements in the field. |
| title | 3D Medical Imaging Segmentation on Non-Contrast CT |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.08361 |