Convolutional neural network architectures and aggregation information models for pulmonary X-ray segmentation

Automatic segmentation of organs and regions in medical imaging is a valuable tool for specialists. This study explores various automatic methods for lung segmentation in X-ray images. First, various neural network architectures are applied for this segmentation task, and subsequently, they are ranked based on their performance through statistical analysis. Then, as some architectures are more suitable for segmenting certain structures or regions of the X-ray, aggregation and consensus methods are studied to fuse the various neural network segmentations, with the aim of obtaining a more complete segmentation. The study reveals that the method based on the WOWA aggregation function, coupled with a maximum-based consensus method, statistically outperforms the individual segmentation provided by the best-performing neural network.