  Lung nodule detection from 3D Computed Tomography scans plays a vital role in
efficient lung cancer screening. Despite the SOTA performance obtained by
recent anchor-based detectors using CNNs for this task, 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. To overcome these problems, we propose a 3D sphere
representation-based center-points matching detection network 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 components: sphere representation and center points
matching. First, to match the nodule annotation in clinical practice, we
replace the commonly used bounding box with our proposed bounding sphere to
represent nodules with the centroid, radius, and local offset in 3D space. A
compatible sphere-based intersection over-union loss function is introduced to
train the lung nodule detection network stably and efficiently. Second, we
empower the network anchor-free by designing a positive center-points selection
and matching process, which naturally discards pre-determined anchor boxes. An
online hard example mining and re-focal loss subsequently enable the CPM
process to be more robust, resulting in more accurate point assignment and
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 framework achieves superior performance
compared with existing anchor-based and anchor-free methods for lung nodule
detection.
