Zhang Jiajing, Gu Lin, Han Guanghui, Liu Xiujian
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan.
Front Oncol. 2022 Jan 28;11:816672. doi: 10.3389/fonc.2021.816672. eCollection 2021.
Radiotherapy is an essential method for treating nasopharyngeal carcinoma (NPC), and the segmentation of NPC is a crucial process affecting the treatment. However, manual segmentation of NPC is inefficient. Besides, the segmentation results of different doctors might vary considerably. To improve the efficiency and the consistency of NPC segmentation, we propose a novel AttR2U-Net model which automatically and accurately segments nasopharyngeal carcinoma from MRI images. This model is based on the classic U-Net and incorporates advanced mechanisms such as spatial attention, residual connection, recurrent convolution, and normalization to improve the segmentation performance. Our model features recurrent convolution and residual connections in each layer to improve its ability to extract details. Moreover, spatial attention is fused into the network by skip connections to pinpoint cancer areas more accurately. Our model achieves a DSC value of 0.816 on the NPC segmentation task and obtains the best performance compared with six other state-of-the-art image segmentation models.
放射治疗是治疗鼻咽癌（NPC）的重要方法，而鼻咽癌的分割是影响治疗的关键过程。然而，鼻咽癌的手动分割效率低下。此外，不同医生的分割结果可能差异很大。为了提高鼻咽癌分割的效率和一致性，我们提出了一种新颖的AttR2U-Net模型，该模型可从MRI图像中自动准确地分割出鼻咽癌。该模型基于经典的U-Net，并结合了空间注意力、残差连接、循环卷积和归一化等先进机制来提高分割性能。我们的模型在每一层都具有循环卷积和残差连接，以提高其提取细节的能力。此外，通过跳跃连接将空间注意力融合到网络中，以更准确地定位癌症区域。我们的模型在鼻咽癌分割任务上的DSC值达到0.816，与其他六个最先进的图像分割模型相比，取得了最佳性能。
