We present a multi-view convolutional neural
networks (MV-CNN) for lung nodule segmentation. The MVCNN
specialized in capturing a diverse set of nodule-sensitive
features from axial, coronal and sagittal views in CT images
simultaneously. The proposed network architecture consists of
three CNN branches, where each branch includes seven stacked
layers and takes multi-scale nodule patches as input. The three
CNN branches are then integrated with a fully connected layer
to predict whether the patch center voxel belongs to the nodule.
The proposed method has been evaluated on 893 nodules from
the public LIDC-IDRI dataset, where ground-truth annotations
and CT imaging data were provided. We showed that MV-CNN
demonstrated encouraging performance for segmenting various
type of nodules including juxta-pleural, cavitary, and nonsolid
nodules, achieving an average dice similarity coefficient
(DSC) of 77.67% and average surface distance (ASD) of 0.24,
outperforming conventional image segmentation approaches.