Improved U-NET network for pulmonary nodules segmentation

Abstract Since pulmonary nodules in CT images are very small and easily confusing with other tissues, there are still many problems in the pulmonary nodule segmentation. This paper presents an improved lung nodule segmentation algorithm based on U-NET network. Firstly, CT images are transformed and normalized, and the lung parenchyma is obtained by simple and efficient morphological method. Then, the U-NET network is improved, which mainly includes the dataset rebuilding, convolutional layer, pooling layer and upsampled layer. And we introduced residual network, which has improved the network training effect. Besides, we designed batch standardization operation, which has speeded up the network training and improves the network stability. Finally, we used the new dataset to train and test the improved U-NET network. A large number of experiments show that the proposed method can effectively improve the segmentation accuracy of pulmonary nodules. It is a great work with theoretical and practical value.

[1]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.

[2]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[3]  Christian Igel,et al.  Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.

[4]  Nima Tajbakhsh,et al.  Computer-Aided Pulmonary Embolism Detection Using a Novel Vessel-Aligned Multi-planar Image Representation and Convolutional Neural Networks , 2015, MICCAI.

[5]  Bram van Ginneken,et al.  A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification , 2009, Medical Image Anal..

[6]  Xujiong Ye,et al.  Graph Cut-based Automatic Segmentation of Lung Nodules using Shape , Intensity , and Spatial Features , 2009 .

[7]  Sun Shen-shen.  Pulmonary Nodule Segmentation Based on EM and Mean-shift , 2009 .

[8]  S. Armato,et al.  Computerized detection of pulmonary nodules on CT scans. , 1999, Radiographics : a review publication of the Radiological Society of North America, Inc.

[9]  D Nazareth,et al.  Level-set segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior. , 2010, Medical physics.

[11]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[12]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[13]  Aly A. Farag,et al.  Data-Driven Lung Nodule Models for Robust Nodule Detection in Chest CT , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[15]  Shinji Yamamoto,et al.  Automatic detection of lung cancers in chest CT images by variable N-Quoit filter , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[16]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[17]  Bram van Ginneken,et al.  Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[18]  Wei Shen,et al.  Multi-scale Convolutional Neural Networks for Lung Nodule Classification , 2015, IPMI.