用改进损失函数训练3D网络，提升全身PET/CT肿瘤分割精度。
AutoPET Challenge III: Testing the Robustness of Generalized Dice Focal Loss trained 3D Residual UNet for FDG and PSMA Lesion Segmentation from Whole-Body PET/CT Images
- 采用3D残差UNet与广义骰子焦点损失联合优化。
- 平均骰子系数达0.6687，假阴性体积仅10.95ml。
- 适合医学影像分析、深度学习医疗应用研究者参考。
自动化分割PET/CT扫描中的癌变病灶是定量图像分析的关键第一步。然而，由于病灶大小、形状和放射性示踪剂摄取的差异，高精度训练深度学习分割模型尤为困难。这些病灶可出现在身体不同部位，常靠近也具有显著摄取的正常器官，使任务更加复杂。本研究采用3D残差UNet模型，并在AutoPET Challenge 2024数据集上使用广义骰子焦点损失函数进行训练。通过五折交叉验证，结合各折模型的平均集成策略，在任务1的预测试阶段，平均集成模型达到平均骰子相似系数（DSC）0.6687，平均假阴性体积（FNV）10.9522 ml，平均假阳性体积（FPV）2.9684 ml。相关算法详情见GitHub仓库：https://github.com/ahxmeds/autosegnet2024.git。训练代码已公开于：https://github.com/ahxmeds/autopet2024.git。
原文摘要 · Abstract (English)
Automated segmentation of cancerous lesions in PET/CT scans is a crucial first step in quantitative image analysis. However, training deep learning models for segmentation with high accuracy is particularly challenging due to the variations in lesion size, shape, and radiotracer uptake. These lesions can appear in different parts of the body, often near healthy organs that also exhibit considerable uptake, making the task even more complex. As a result, creating an effective segmentation model for routine PET/CT image analysis is challenging. In this study, we utilized a 3D Residual UNet model and employed the Generalized Dice Focal Loss function to train the model on the AutoPET Challenge 2024 dataset. We conducted a 5-fold cross-validation and used an average ensembling technique using the models from the five folds. In the preliminary test phase for Task-1, the average ensemble achieved a mean Dice Similarity Coefficient (DSC) of 0.6687, mean false negative volume (FNV) of 10.9522 ml and mean false positive volume (FPV) 2.9684 ml. More details about the algorithm can be found on our GitHub repository: https://github.com/ahxmeds/autosegnet2024.git. The training code has been shared via the repository: https://github.com/ahxmeds/autopet2024.git.
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