Fine-grained lung nodule segmentation with pyramid deconvolutional neural network

Abstract

An improved pyramid deconvolutional neural network is proposed to fine-grained segment pulmonary nodules of CT scan images. The fully convolutional neural network (FCN) can train images end-to-end, pixel-to-pixel, realizing object detection, segmentation and classification in one single CNN structure. However, the original FCN is utilized by the natural object tasks, which can hardly maintain the precision degree required by the medical images. To further improve the detection precision and segment accuracy, we improve the FCN by fusing more pooling layers, because the deconvolution of higher convolution layers give the coarser segmentations and lower convolution layers generate detail contour. The experiment is based on LIDC- IDRI datasets. Tenfold cross-validation is used to train and evaluate the performance. The experiment shows that the detection precise and the fineness of segmentation ascend with the number of the fused pooling layers. The detection rate can be achieved as high as 0.931 ± 0.042. Meanwhile, for the segmentation performance evaluation, the score of intersection over Union (IoU) is applied, reaching 0.628 ± 0.065. And the overlap rate (i.e. the overlap percentage of the segment result compared with the original label) is also calculated. The same as the detect accuracy, the improved architecture, which fuses more pooling layers, achieves the highest overlap rate, which is 0.739 ± 0.076.