  The human lung is a complex respiratory organ, consisting of five distinct
anatomic compartments called lobes. Accurate and automatic segmentation of
these pulmonary lobes from computed tomography (CT) images is of clinical
importance for lung disease assessment and treatment planning. However, this
task is challenging due to ambiguous lobar boundaries, anatomical variations
and pathological deformations. In this paper, we propose a high-resolution and
efficient 3D fully convolutional network to automatically segment the lobes. We
refer to the network as Pulmonary Lobe Segmentation Network (PLS-Net), which is
designed to efficiently exploit 3D spatial and contextual information from
high-resolution volumetric CT images for effective volume-to-volume learning
and inference. The PLS-Net is based on an asymmetric encoder-decoder
architecture with three novel components: (i) 3D depthwise separable
convolutions to improve the network efficiency by factorising each regular 3D
convolution into two simpler operations; (ii) dilated residual dense blocks to
efficiently expand the receptive field of the network and aggregate multi-scale
contextual information for segmentation; and (iii) input reinforcement at each
downsampled resolution to compensate for the loss of spatial information due to
convolutional and downsampling operations. We evaluated the proposed PLS-Net on
a multi-institutional dataset that consists of 210 CT images acquired from
patients with a wide range of lung abnormalities. Experimental results show
that our PLS-Net achieves state-of-the-art performance with better
computational efficiency. Further experiments confirm the effectiveness of each
novel component of the PLS-Net.
