Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909320977842176 |
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| author | Ren, Jintao Li, Muheng Korreman, Stine Sofia |
| author_facet | Ren, Jintao Li, Muheng Korreman, Stine Sofia |
| contents | This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13410 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging Ren, Jintao Li, Muheng Korreman, Stine Sofia Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET). |
| title | Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics |
| url | https://arxiv.org/abs/2409.13410 |