Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model. (December 2020)

• Record Type: Journal Article
• Authors: Yu, Dingding; Zhang, Kaijie; Huang, Lingyan; Zhao, Bonan; Zhang, Xiaoshan; Guo, Xin; Li, Miaomiao; Gu, Zheng; Fu, Guosheng; Hu, Minchun; Ping, Yan; Sheng, Ye; Liu, Zhenjie; Hu, Xianliang; Zhao, Ruiyi
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Deep learning -- Multi-task learning -- Picc -- Segmentation -- Tip detection -- Chest x-ray images; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105674
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel multi-task deep learning framework to detect PICC automatically through X-ray images is proposed. Long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. The proposed model can help nurses to identify the position of PICC and take proper actions as soon as the complications occur. Compared with other popular models, our model achieved the best results, indicating the effectiveness of our model. Abstract: Background and Objective: Peripherally inserted central catheter (PICC) is a novel drug delivery mode which has been widely used in clinical practice. However, long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. Clinically, the postoperative care of PICC is mainly completed by nurses. However, they cannot recognize the correct position of PICC from X-ray chest images as soon as the complications happen, which may lead to improper treatment. Therefore, it is necessary to identify the position of the PICC catheter as soon as these complications occur. Here we proposed a novel multi-task deep learning framework to detect PICC automatically through X-ray images, which could help nurses to solve this problem. Methods: We collected 348 X-ray chest images from 326 patients with visible PICC. Then we proposed a multi-task deep learning framework for line segmentation and tip detection of PICC catheters simultaneously. The proposed deep learning model is composed of an extraction structure and three routes, an up-sampling route for segmentation, an RPNs route, and an RoI Pooling route for detection. We further compared the effectiveness of our model with the models previously proposed. Results: In the catheter segmentation task, 300 X-ray images were utilized for training the model, then 48 images were tested. In the tip detection task, 154 X-ray images were used for retraining and 20 images were used in the test. Our model achieved generally better results among several popular deep learning models previously proposed. Conclusions: We proposed a multi-task deep learning model that could segment the catheter and detect the tip of PICC simultaneously from X-ray chest images. This model could help nurses to recognize the correct position of PICC, and therefore, to handle the potential complications properly.