Reliable detection of lymph nodes in whole pelvic for radiotherapy. (April 2022)

• Record Type: Journal Article
• Authors: Sun, Ying; Zhang, Xiuming; Jiang, Yuting; Wang, Yuening; Kang, Zheng; Qiao, Wei; Xu, Hanzi; Tao, Chao; Liu, Xiaojun; Yuan, Jie
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Lymph nodes detection -- CT images -- Contextual information; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103501
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A reliable method for detection of pelvic lymph nodes is proposed. The proposed method performs well in the complex pelvic environment. A combining contextual approach is used to reduce false positives. Post-processing method based on structural similarity is presented for better performance. Abstract: Accurate detection of the lymph nodes in patients with cervical cancer enables more targeted treatment. For drainage or radiotherapy of postoperative adjuvant, clinicians will manually label the lymph nodes in Computed tomography (CT) images. However, due to the complex environment and wide area of pelvic, reliable detection of lymph nodes is challenging because of the changeable shapes, sizes, distributions. Furthermore, the confusion of blood vessels and tissues in CT images also causes difficulties when labeling lymph nodes. In this study, a reliable convolutional neural network (CNN) based detection approach is proposed to distinguish lymph nodes in the middle abdomen and pelvic cavity between CT image sequences. Combining both local and global contextual information in CT image sequences can avoiding complex three-dimensional (3D) computation. Different scale transformation and structural similarity-based processing method ensure the accuracy to distinguish lymph nodes and blood vessels. Experimental results compared with those given by radiologists and other CNN based methods proved that our proposed method could locate lymph nodes with 98.29% accuracy and 94.64% recall rate when detecting 22, 846 clinical abdominal CT images, which proved its potential application value for radiotherapy of cervical cancer.