  Automatic and accurate lung nodule detection from 3D Computed Tomography
scans plays a vital role in efficient lung cancer screening. Despite the
state-of-the-art performance obtained by recent anchor-based detectors using
Convolutional Neural Networks, they require predetermined anchor parameters
such as the size, number, and aspect ratio of anchors, and have limited
robustness when dealing with lung nodules with a massive variety of sizes. We
propose a 3D sphere representation-based center-points matching detection
network (SCPM-Net) that is anchor-free and automatically predicts the position,
radius, and offset of nodules without the manual design of nodule/anchor
parameters. The SCPM-Net consists of two novel pillars: sphere representation
and center points matching. To mimic the nodule annotation in clinical
practice, we replace the conventional bounding box with the newly proposed
bounding sphere. A compatible sphere-based intersection over-union loss
function is introduced to train the lung nodule detection network stably and
efficiently.We empower the network anchor-free by designing a positive
center-points selection and matching (CPM) process, which naturally discards
pre-determined anchor boxes. An online hard example mining and re-focal loss
subsequently enable the CPM process more robust, resulting in more accurate
point assignment and the mitigation of class imbalance. In addition, to better
capture spatial information and 3D context for the detection, we propose to
fuse multi-level spatial coordinate maps with the feature extractor and combine
them with 3D squeeze-and-excitation attention modules. Experimental results on
the LUNA16 dataset showed that our proposed SCPM-Net framework achieves
superior performance compared with existing used anchor-based and anchor-free
methods for lung nodule detection.
