The aim of this study is to evaluate the feasibility of a deep learning tumor segmentation network trained by merged tumor dataset. PET-CT datasets for head-and-neck (H&N) and limb tumors were used to train three different networks: H&N, Limb, and merged (H&N + Limb). Dice similarity coefficient (DSC) of the merged network (0.89) in limb tumors was the same as that of the Limb network. In H&N tumor, DSC of the merged network (0.72) was higher than that of the H&N network (0.69). We found that the merged network could be applied simultaneously in H&N and limb tumor segmentation.

中文翻译：

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合并肿瘤数据集的深度学习肿瘤分割的可行性研究：头颈部和四肢

本研究的目的是评估由合并肿瘤数据集训练的深度学习肿瘤分割网络的可行性。头颈部 (H&N) 和肢体肿瘤的 PET-CT 数据集用于训练三种不同的网络：H&N、Limb 和合并 (H&N + Limb)。肢体肿瘤中合并网络的骰子相似系数（DSC）（0.89）与肢体网络的相同。在H&N肿瘤中，融合网络的DSC（0.72）高于H&N网络的DSC（0.69）。我们发现合并网络可以同时应用于 H&N 和肢体肿瘤分割。