Title: End-to-end Lung Nodule Detection in Computed Tomography,
Abstract: Computer aided diagnostic (CAD) system is crucial for modern med-ical
imaging. But almost all CAD systems operate on reconstructed images, which were
optimized for radiologists. Computer vision can capture features that is subtle
to human observers, so it is desirable to design a CAD system op-erating on the
raw data. In this paper, we proposed a deep-neural-network-based detection
system for lung nodule detection in computed tomography (CT). A
primal-dual-type deep reconstruction network was applied first to convert the
raw data to the image space, followed by a 3-dimensional convolutional neural
network (3D-CNN) for the nodule detection. For efficient network training, the
deep reconstruction network and the CNN detector was trained sequentially
first, then followed by one epoch of end-to-end fine tuning. The method was
evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI)
with simulated forward projections. With 144 multi-slice fanbeam pro-jections,
the proposed end-to-end detector could achieve comparable sensitivity with the
reference detector, which was trained and applied on the fully-sampled image
data. It also demonstrated superior detection performance compared to detectors
trained on the reconstructed images. The proposed method is general and could
be expanded to most detection tasks in medical imaging.