Segmentation of lung field in HRCT images using U-net based fully convolutional networks

Abstract

Segmentation is a preliminary step towards the development of automated computer aided diagnosis system (CAD). The system accuracy and efficiency primarily depend on the accurate segmentation result. Effective lung field segmentation is major challenging task, especially in the presence of different types of interstitial lung diseases (ILD). At present, high resolution computed tomography (HRCT) is considered to be the best imaging modality to observe ILD patterns. The most common patterns based on their textural appearances are consolidation, emphysema, fibrosis, ground glass opacity (GGO), reticulation and micronodules. In this paper, automatic lung field segmentation of pathological lung has been done using U-Net based deep convolutional networks. Our proposed model has been evaluated on publicly available MedGIFT database. The segmentation result was evaluated in terms of the dice similarity coefficient (DSC). Finally, the experimental results obtained on 330 testing images of different patterns achieving 94% of average DSC.