Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans

• URL: http://arxiv.org/abs/2210.07490v1
• Date: Fri, 14 Oct 2022 03:37:18 GMT
• Title: Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
• Authors: Jin Ye, Haoyu Wang, Ziyan Huang, Zhongying Deng, Yanzhou Su, Can Tu, Qian Wu, Yuncheng Yang, Meng Wei, Jingqi Niu, and Junjun He
• Abstract summary: Since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. In this paper, we explore the potential of U-Net for lesion segmentation in whole-body FDG-PET/CT scans from three aspects, including network architecture, data preprocessing, and data augmentation. Our method achieves first place in both preliminary and final leaderboards of the autoPET 2022 challenge.
• Score: 16.93163630413171
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. To handle this challenge, PET is commonly combined with Computed Tomography~(CT), with the CT used to obtain the anatomic structure of the patient. The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results. %Computed tomography~(CT) is a popular modality to illustrate the anatomic structure of the patient. The combination of PET and CT is promising to handle this challenge by utilizing metabolic and anatomic information. In this paper, we explore the potential of U-Net for lesion segmentation in whole-body FDG-PET/CT scans from three aspects, including network architecture, data preprocessing, and data augmentation. The experimental results demonstrate that the vanilla U-Net with proper input shape can achieve satisfactory performance. Specifically, our method achieves first place in both preliminary and final leaderboards of the autoPET 2022 challenge. Our code is available at https://github.com/Yejin0111/autoPET2022_Blackbean.

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