A robust approach for endotracheal tube localization in chest radiographs - Hsu, Chung-Chian; Ameri, Rasoul; Lin, Chih-Wen; He, Jia-Shiang; Biyari, Meghdad; Yarahmadi, Atefeh; Band, Shahab S.; Lin, Tin-Kwang; Fan, Wen-Lin (Frontiers in Artificial Intelligence, 2023)
A precise detection and localization of the Endotracheal tube (ETT) is essential for patients in the emergency room using chest radiographs. A robust deep learning model based on U-Net++ architecture is presented for accurate segmentation and localization of the ETT. Different types of loss functions related to distribution and region-based loss functions are evaluated in this paper. Then, various integrations of distribution and region-based loss functions (compound loss function) have been applied to obtain the best intersection over union (IOU) for ETT segmentation. The Main purpose of the presented study is to maximize IOU for ETT segmentation, and also minimize the error that has been taken in to account during calculation of distance between the real and predicted ETT by obtaining the best integration of the distribution and region loss functions (compound loss function) for training the U-Net++ model. We analyzed the performance of our model using chest radiograph from Dalin Tzu Chi Hospital in Taiwan. The results of applying the integration of distribution-based and region-based loss functions on Dalin Tzu Chi Hospital dataset show enhanced segmentation performance compared to other single loss functions. Moreover, according to the obtained results, the combination of Matthews Correlation Coefficient (MCC) and Tversky loss functions, which is a hybrid loss function, has shown the best performance on ETT segmentation based on its ground truth among the others with the IOU value of 0.8683.