Automatic Detection of Lung Nodules Using 3D Deep Convolutional Neural Networks

被引：10

作者：

Fu L. ^{[1
]}

Ma J. ^{[1
]}

Chen Y. ^{[1
]}

Larsson R. ^{[1
,4
]}

Zhao J. ^{[1
,2
,3
]}

机构：

[1] School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai

[2] SJTU-UIH Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai

[3] MED-X Research Institute, Shanghai Jiao Tong University, Shanghai

[4] School of Technology and Health, KTH Royal Institute of Technology, Stockholm

来源：

Journal of Shanghai Jiaotong University (Science) | 2019年 / 24卷 / 04期

关键词：

A; computer-aided detection (CAD); convolutional neural network (CNN); fully convolutional neural network (FCN); lung nodule detection; R; 318;

D O I：

10.1007/s12204-019-2084-4

中图分类号：

学科分类号：

摘要：

Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks (CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network (FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection. © 2019, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：517 / 523

页数：6

共 31 条

[1]

Siegel R.L., Miller K.D., Jemal A., Cancer statistics, 2016 [J], CA: A Cancer Journal for Clinicians, 66, 1, pp. 7-30, (2016)

[2]

Wang B., Tian X.D., Wang Q., Et al., Pulmonary nodule detection in CT images based on shape constraint CV model [J], Medical Physics, 42, 3, pp. 1241-1254, (2015)

[3]

Li Q., Sone S., Doi K., Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans [J], Medical Physics, 30, 8, pp. 2040-2051, (2003)

[4]

Tan M., Deklerck R., Jansen B., Et al., A novel computer-aided lung nodule detection system for CT images [J], Medical Physics, 38, 10, pp. 5630-5645, (2011)

[5]

Messay T., Hardie R.C., Rogers S.K., A new computationally efficient CAD system for pulmonary nodule detection in CT imagery [J], Medical Image Analysis, 14, 3, pp. 390-406, (2010)

[6]

Setio A.A.A., Ciompi F., Litjens G., Et al., Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks [J], IEEE Transactions on Medical Imaging, 35, 5, pp. 1160-1169, (2016)

[7]

Murphy K., Van, Ginneken B., Schilham A.M.R., Et al., A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification [J], Medical Image Analysis, 13, 5, pp. 757-770, (2009)

[8]

Jacobs C., Van, Rikxoort E.M., Twellmann T., Et al., Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images [J], Medical Image Analysis, 18, 2, pp. 374-384, (2014)

[9]

Setio A.A.A., Jacobs C., Gelderblom J., Et al., Automatic detection of large pulmonary solid nodules in thoracic CT images [J], Medical Physics, 42, 10, pp. 5642-5653, (2015)

[10]

Ciompi F., Jacobs C., Scholten E.T., Et al., Bagof-frequencies: A descriptor of pulmonary nodules in computed tomography images [J], IEEE Transactions on Medical Imaging, 34, 4, pp. 962-973, (2015)

← 1 2 3 4 →