Abstract
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.
Highlights
Nasopharyngeal cancer is a common malignant tumor occurring in the top and sidewalls of the nasopharyngeal cavity, with 833,019 new cases of nasopharyngeal cancer and 468,745 deaths in China alone during 2015 [1]
Based on the U-Net structure, we propose AttR2U-Net and integrate several advanced deep learning method (Figure 1C), including spatial attention, residual connection, recurrent convolution, and normalization
The results show that our model is superior to other models for nasopharyngeal carcinoma segmentation
Summary
Nasopharyngeal cancer is a common malignant tumor occurring in the top and sidewalls of the nasopharyngeal cavity, with 833,019 new cases of nasopharyngeal cancer and 468,745 deaths in China alone during 2015 [1]. Nasopharyngeal cancer affects a wide range of areas, from the nasal cavity forward to the conus, down to the oropharynx, and up to the skull. It is mainly located in the central part of the head [2], making it difficult to treat with common surgical treatments. The lesion segmentation is one of the most critical factors affecting the effectiveness of radiotherapy. Traditional manual segmentation (Figure 1A) has three main drawbacks. The segmentation still currently relies on specialized physicians to manually segment nasopharyngeal
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