Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929500383608832 |
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| author | Xue, Qiaoyi Feng, Youdan Liu, Jiayi Xu, Tianming Shen, Kaixin Shen, Chuyun Shi, Yuhang |
| author_facet | Xue, Qiaoyi Feng, Youdan Liu, Jiayi Xu, Tianming Shen, Kaixin Shen, Chuyun Shi, Yuhang |
| contents | This study explores a workflow for automated segmentation of lesions in FDG and PSMA PET/CT images. Due to the substantial differences in image characteristics between FDG and PSMA, specialized preprocessing steps are required. Utilizing YOLOv8 for data classification, the FDG and PSMA images are preprocessed separately before feeding them into the segmentation models, aiming to improve lesion segmentation accuracy. The study focuses on evaluating the performance of automated segmentation workflow for multitracer PET images. The findings are expected to provide critical insights for enhancing diagnostic workflows and patient-specific treatment plans. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/AP2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09766 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting Xue, Qiaoyi Feng, Youdan Liu, Jiayi Xu, Tianming Shen, Kaixin Shen, Chuyun Shi, Yuhang Computer Vision and Pattern Recognition Artificial Intelligence This study explores a workflow for automated segmentation of lesions in FDG and PSMA PET/CT images. Due to the substantial differences in image characteristics between FDG and PSMA, specialized preprocessing steps are required. Utilizing YOLOv8 for data classification, the FDG and PSMA images are preprocessed separately before feeding them into the segmentation models, aiming to improve lesion segmentation accuracy. The study focuses on evaluating the performance of automated segmentation workflow for multitracer PET images. The findings are expected to provide critical insights for enhancing diagnostic workflows and patient-specific treatment plans. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/AP2024. |
| title | Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.09766 |