The lead wire marker detection model
In this study, we developed a DL model to detect the lead wire marker on CT images. The lead wire marker detection model was built based on HRNet [The overall workflow of our study. Before reaching the network, the CT images were clipped into [-140, 210] and then normalized to [0, 1]. For the prediction of each slice, the target slice and its adjacent two slices were combined to form three channels. The Adam optimizer was used to minimize the mean square error loss, with an initial learning rate of 0.0001. The model was trained on the training cohort for 2000 epochs, and the model that reached the lowest loss in the validation cohort was used.After the model was trained, the mean and standard deviation of the distance between the location of the predicted marker and the labeled marker were measured, and the average precision (AP) and average recall (AR) were calculated.