Catheter segmentation in chest x-ray: improving imbalanced segmentation with a class frequency weighted loss function

Abstract

Deep convolutional neural networks (CNNs) are widely used in medical image segmentation, but they tend to ignore smaller foreground classes in the training process and thus degrade segmentation accuracy, which is known as the class imbalance problem. Central venous catheter (CVC) segmentation suffers from such problems, leading to low accuracy. The purpose of this study is to address the class imbalance problem in CNN training for segmenting the right internal jugular lines (RIJLs), the most common type of CVCs, in chest X-ray (CXR) images. We applied an inverse class frequency weight to the standard Dice loss to formulate a class frequency weighted Dice loss (CFDL) function to train the CNNs. A U-net based segmentation model was constructed with multichannel pre-processing, including normalization, denoising, and histogram equalization, and post-processing including thresholding segmentation candidates and interpolating discontinued line segmentations. The segmentation model was trained on CXR images with the Dice loss and the CFDL respectively. A separate test set of images were used to evaluate the CNN output performances with the Dice Similarity Coefficient (DSC). Between the Dice loss and the CFDL, the CFDL-trained CNN generated segmentation results with a mean DSC of 0.581 on the separate test set, which indicates a statistically significant difference (p=0.001) from the Dice loss-trained CNN outputs with a mean DSC of 0.562. The inverse class frequency weighted Dice loss function improved the RIJL segmentation with a U-net to the state-of-the-art performance.