Efficient convolutional neural networks for multi-planar lung nodule detection: improvement on small nodule identification

01/13/2020
by   Sunyi Zheng, et al.
46

We propose a multi-planar pulmonary nodule detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. After ten-fold cross-validation, our proposed system achieves a sensitivity of 95.3 and a sensitivity of 96.2 difficult to detect small nodules (i.e. nodules with a diameter < 6 mm), our designed CAD system reaches a sensitivity of 93.8 nodules at an overall false positive rate of 0.5 (1.0) false positives/scan. At the nodule candidate detection stage, the proposed system detected 98.1 nodules after merging the predictions from all three planes. Using only the 1 mm axial slices resulted in the detection of 91.1 than that of utilizing solely the coronal or sagittal slices. The results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Our approach achieves state-of-the-art performance on this dataset, which demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset