论文标题
在CT和PET图像中组合CNN和混合动力轮廓用于头颈部肿瘤分割
Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images
论文作者
论文摘要
头颈肿瘤的自动分割在放射线分析中起重要作用。在这篇简短的论文中，我们提出了一种基于卷积神经网络（CNN）和混合活动轮廓的组合的PET和CT图像的头颈肿瘤的自动分割方法。具体而言，我们首先引入多通道3D U-NET，以将肿瘤与串联PET和CT图像分割。然后，我们通过模型集合来估计分割不确定性，并定义分割质量评分，以选择高不确定性的案例。最后，我们开发了一个混合主动轮廓模型，以完善高不确定性病例。我们的方法在MICCAI 2020 Hecktor挑战中排名第二，平均骰子相似性系数，精度和召回率分别为0.752、0.838和0.717。
Automatic segmentation of head and neck tumors plays an important role in radiomics analysis. In this short paper, we propose an automatic segmentation method for head and neck tumors from PET and CT images based on the combination of convolutional neural networks (CNNs) and hybrid active contours. Specifically, we first introduce a multi-channel 3D U-Net to segment the tumor with the concatenated PET and CT images. Then, we estimate the segmentation uncertainty by model ensembles and define a segmentation quality score to select the cases with high uncertainties. Finally, we develop a hybrid active contour model to refine the high uncertainty cases. Our method ranked second place in the MICCAI 2020 HECKTOR challenge with average Dice Similarity Coefficient, precision, and recall of 0.752, 0.838, and 0.717, respectively.
