Abstract
Automatic and accurate lung nodule detection from Computed Tomography (CT) 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 (CNNs) for this task, they require pre-determined 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 this problem, we propose a 3D center-points matching detection network (CPM-Net) that is anchor-free and automatically predicts the position, size and aspect ratio of nodules without manual design of nodule/anchor parameters. The CPM-Net uses center-points matching strategy to find center-points, and then uses features of these points correspondingly to regress the size of the bounding box of nodule and local offset of the center points. 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 it with 3D squeeze-and-excitation attention modules. To deal with the enormous imbalance between the number of positive and negative samples during center points matching, we propose a hybrid method of adaptive points mining and re-focal loss. Experimental results on LUNA16 dataset showed that our proposed CPM-Net achieved superior performance for lung nodule detection compared with state-of-the-art anchor-based methods.
T. Song and J. Chen—Equal contribution.
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Song, T. et al. (2020). CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_53
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DOI: https://doi.org/10.1007/978-3-030-59725-2_53
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