AutoPET Challenge: Tumour Synthesis for Data Augmentation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866912024878907392 |
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| author | Chan, Lap Yan Lennon Li, Chenxin Yuan, Yixuan |
| author_facet | Chan, Lap Yan Lennon Li, Chenxin Yuan, Yixuan |
| contents | Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models. In this paper, we explore the potential of leveraging the deep prior from a generative model to serve as a data augmenter for automated lesion segmentation in PET/CT scans. We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. We then compare the performance of segmentation models trained on the original and augmented datasets. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach. In a nutshell, this work presents a promising direction for improving lesion segmentation in whole-body PET/CT scans with limited datasets, potentially enhancing the accuracy and reliability of cancer diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08068 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoPET Challenge: Tumour Synthesis for Data Augmentation Chan, Lap Yan Lennon Li, Chenxin Yuan, Yixuan Image and Video Processing Computer Vision and Pattern Recognition Medical Physics Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models. In this paper, we explore the potential of leveraging the deep prior from a generative model to serve as a data augmenter for automated lesion segmentation in PET/CT scans. We adapt the DiffTumor method, originally designed for CT images, to generate synthetic PET-CT images with lesions. Our approach trains the generative model on the AutoPET dataset and uses it to expand the training data. We then compare the performance of segmentation models trained on the original and augmented datasets. Our findings show that the model trained on the augmented dataset achieves a higher Dice score, demonstrating the potential of our data augmentation approach. In a nutshell, this work presents a promising direction for improving lesion segmentation in whole-body PET/CT scans with limited datasets, potentially enhancing the accuracy and reliability of cancer diagnostics. |
| title | AutoPET Challenge: Tumour Synthesis for Data Augmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2409.08068 |