Automatic segmentation of the tumor in nonsmall‐cell lung cancer by combining coarse and fine segmentation

• Published in: Medical physics (Lancaster) 2023-06, Vol.50 (6), p.3549-3559
• Main Authors: Zhang, Fuli, Wang, Qiusheng, Fan, Enyu, Lu, Na, Chen, Diandian, Jiang, Huayong, Wang, Yadi
• Format: Article
• Language: English
• Subjects: automatic segmentation; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - radiotherapy; coarse segmentation; convolutional neural network; fine segmentation; Humans; Image Processing, Computer-Assisted - methods; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - radiotherapy; Neural Networks, Computer; nonsmall‐cell lung cancer; Radiotherapy Planning, Computer-Assisted - methods
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.16158

Objectives Radiotherapy plays an important role in the treatment of nonsmall‐cell lung cancer (NSCLC). Accurate delineation of tumor is the key to successful radiotherapy. Compared with the commonly used manual delineation ways, which are time‐consuming and laborious, the automatic segmentation methods based on deep learning can greatly improve the treatment efficiency. Methods In this paper, we introduce an automatic segmentation method by combining coarse and fine segmentations for NSCLC. Coarse segmentation network is the first level, identifing the rough region of the tumor. In this network, according to the tissue structure distribution of the thoracic cavity where tumor is located, we designed a competition method between tumors and organs at risk (OARs), which can increase the proportion of the identified tumor covering the ground truth and reduce false identification. Fine segmentation network is the second level, carrying out precise segmentation on the results of the coarse level. These two networks are independent of each other during training. When they are used, morphological processing of small scale corrosion and large scale expansion is used for the coarse segmentation results, and the outcomes are sent to the fine segmentation part as input, so as to achieve the complementary advantages of the two networks. Results In the experiment, CT images of 200 patients with NSCLC are used to train the network, and CT images of 60 patients are used to test. Finally, our method produced the Dice similarity coefficient of 0.78 ± 0.10. Conclusions The experimental results show that the proposed method can accurately segment the tumor with NSCLC, and can also provide support for clinical diagnosis and treatment.