Nasopharyngeal Organ Segmentation Algorithm Based on Dilated Convolution Feature Pyramid

Pan, Xiaoying; Dai, Dong; Wang, Hongyu; Liu, Xingxing; Bai, Weidong

doi:10.1007/978-981-16-6963-7_4

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Since the pioneering CNN algorithm by Lecun et al, in 1990, more and more improved CNN algorithms for image segmentation have been proposed. Pan et al [35] improved the typical CNN network by designing dilated convolution at each layer of the FPN to obtain contextual associations, which was applied to nasopharyngeal organ target segmentation. Some other scholars segment nasopharyngeal carcinoma by improving CNN into the CNNbased method with three-dimensional filters [36][37][38].…”

Section: Fully-supervisedmentioning

confidence: 99%

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

Chen

Han

Lin

et al. 2022

Sensors

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Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

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