Automatic lung segmentation: a comparison of anatomical and machine learning approaches

The aim of this work is to develop an automatic lung segmentation system, capable of segmenting the lung into apical, middle and basal regions, along the axial plane of the body. An accurate segmentation of the lung is important for diagnosis of diffuse lung diseases, as well as to characterise and track particular diseases. In this paper, we compare the two strategies we have developed. The anatomy based approach uses anatomical landmark detection to define the separation points between the regions, whilst the machine learning approach uses lung shape, size and location properties, to classify a given lung into the appropriate region.