Feasibility Study of Deep Learning Tumor Segmentation for a Merged Tumor Dataset: Head & Neck and Limbs

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.