  Computed tomography (CT) generates a stack of cross-sectional images covering
a region of the body. The visual assessment of these images for the
identification of potential abnormalities is a challenging and time consuming
task due to the large amount of information that needs to be processed. In this
article we propose a deep artificial neural network architecture, ReCTnet, for
the fully-automated detection of pulmonary nodules in CT scans. The
architecture learns to distinguish nodules and normal structures at the pixel
level and generates three-dimensional probability maps highlighting areas that
are likely to harbour the objects of interest. Convolutional and recurrent
layers are combined to learn expressive image representations exploiting the
spatial dependencies across axial slices. We demonstrate that leveraging
intra-slice dependencies substantially increases the sensitivity to detect
pulmonary nodules without inflating the false positive rate. On the publicly
available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet
reaches a detection sensitivity of 90.5% with an average of 4.5 false positives
per scan. Comparisons with a competing multi-channel convolutional neural
network for multi-slice segmentation and other published methodologies using
the same dataset provide evidence that ReCTnet offers significant performance
gains.
