Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

26-04-2019 | Original Article

Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks

Authors: Li Gong, Shan Jiang, Zhiyong Yang, Guobin Zhang, Lu Wang

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Purpose

Pulmonary nodule detection has great significance for early treating lung cancer and increasing patient survival. This work presents a novel automated computer-aided detection scheme for pulmonary nodules based on deep convolutional neural networks (DCNNs).

Methods

The proposed approach employs 3D DCNNs based on squeeze-and-excitation network and residual network (SE-ResNet) for pulmonary nodule candidate detection and false-positive reduction. Specifically, a 3D region proposal network with a U-Net-like structure is designed for detecting pulmonary nodule candidates. For the subsequent false-positive reduction, a 3D SE-ResNet-based classifier is presented to accurately discriminate the true nodules from candidates. The 3D SE-ResNet modules boost the representational power of the network by adaptively recalibrating channel-wise residual feature responses. Both models utilize 3D SE-ResNet modules to learn nodule features effectively and improve nodule detection performance.

Results

On the public available lung nodule analysis 2016 dataset with 888 scans included, the proposed method reaches high detection sensitivities of 93.6% and 95.7% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric score of 0.904 is achieved. The proposed method has the capability to detect multi-size nodules, especially the extremely small nodules.

Conclusion

In this paper, a 3D DCNNs framework based on 3D SE-ResNet modules is proposed to detect pulmonary nodules in chest CT images accurately. Experimental results demonstrate superior effectiveness of the proposed approach in pulmonary nodule detection task.

previous article Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models

next article Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Torre LA, Siegel RL, Jemal A (2016) Lung cancer statistics. Adv Exp Med Biol 893:1–19. https://doi.org/10.1007/978-3-319-24223-1_1 CrossRefPubMed

Henschke CI (2000) Early lung cancer action project: overall design and findings from baseline screening. Cancer 89(11):2474–2482. https://doi.org/10.1002/1097-0142(20001201)89:11+%3c2474:aid-cncr26%3e3.0.co;2-2 CrossRefPubMed

Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30. https://doi.org/10.3322/caac.21442 CrossRef

National Lung Screening Trial Research T, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365(5):395–409. https://doi.org/10.1056/nejmoa1102873 CrossRef

Karki A, Shah R, Fein A (2017) Multiple pulmonary nodules in malignancy. Curr Opin Pulm Med 23(4):285–289. https://doi.org/10.1097/MCP.0000000000000393 CrossRefPubMed

Zhang G, Jiang S, Yang Z, Gong L, Ma X, Zhou Z, Bao C, Liu Q (2018) Automatic nodule detection for lung cancer in CT images: a review. Comput Biol Med 103:287–300. https://doi.org/10.1016/j.compbiomed.2018.10.033 CrossRefPubMed

Lu L, Tan Y, Schwartz LH, Zhao B (2015) Hybrid detection of lung nodules on CT scan images. Med Phys 42(9):5042–5054. https://doi.org/10.1118/1.4927573 CrossRefPubMed

Saien S, Moghaddam HA, Fathian M (2018) A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection. Int J Comput Assist Radiol Surg 13(3):397–409. https://doi.org/10.1007/s11548-017-1656-8 CrossRefPubMed

Gong J, Liu JY, Wang LJ, Sun XW, Zheng B, Nie SD (2018) Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Phys Med 46:124–133. https://doi.org/10.1016/j.ejmp.2018.01.019 CrossRefPubMed

10.

Schmidhuber J (2014) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003 CrossRefPubMed

11.

Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. IEEE CVPR 2015:1–9. https://doi.org/10.1109/CVPR.2015.7298594 CrossRef

12.

Setio AAA, Traverso A, de Bel T, Berens MSN, Bogaard CVD, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, Gugten RV, Heng PA, Jansen B, de Kaste MMJ, Kotov V, Lin JY, Manders J, Sonora-Mengana A, Garcia-Naranjo JC, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ET, Scholten L, Snoeren MM, Torres EL, Vandemeulebroucke J, Walasek N, Zuidhof GCA, Ginneken BV, Jacobs C (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal 42:1. https://doi.org/10.1016/j.media.2017.06.015 CrossRefPubMed

13.

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005 CrossRef

14.

Murphy A, Skalski M, Gaillard F (2018) The utilisation of convolutional neural networks in detecting pulmonary nodules: a review. Br J Radiol 91(1090):20180028. https://doi.org/10.1259/bjr.20180028 CrossRefPubMedPubMedCentral

15.

Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q (2018) Deep learning for image-based cancer detection and diagnosis—a survey. Pattern Recognit 83:134–149. https://doi.org/10.1016/j.patcog.2018.05.014 CrossRef

16.

Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MM, Naqibullah M, Sanchez CI, van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169. https://doi.org/10.1109/TMI.2016.2536809 CrossRefPubMed

17.

Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. MICCAI 2015:234–241. https://doi.org/10.1007/978-3-319-24574-4_28 CrossRef

18.

Zagoruyko S, Komodakis N (2016) Wide residual networks. In: BMVC 2016

19.

Huang XJ, Shan JJ, Vaidya V (2017) Lung nodule detection in CT using 3D convolutional neural networks. IEEE ISBI 2017:379–383. https://doi.org/10.1109/ISBI.2017.7950542 CrossRef

20.

Dou Q, Chen H, Yu LQ, Qin J, Heng PA (2017) Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Bio Med Eng 62(7):1558–1567. https://doi.org/10.1109/TBME.2016.2613502 CrossRef

21.

Dou Q, Chen H, Jin YM, Heng PA (2017) Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning. MICCAI 2017(10435):630–638. https://doi.org/10.1007/978-3-319-66179-7_72 CrossRef

22.

He K, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. IEEE CVPR 2016:770–778. https://doi.org/10.1109/CVPR.2016.90 CrossRef

23.

Jin HS, Li ZY, Tong RF, Lin LF (2018) A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Med Phys 45(5):2097–2107. https://doi.org/10.1002/mp.12846 CrossRefPubMed

24.

Ding J, Li AX, Hu ZQ, Wang LW (2017) Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. MICCAI 2017(10435):559–567. https://doi.org/10.1007/978-3-319-66179-7_64 CrossRef

25.

Ren SQ, He KM, Girshick R, Sun J (2016) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE TPAMI 2016:1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031 CrossRef

26.

Zhu WT, Liu CC, Fan W, Xie XH (2018) DeepLung: 3D deep convolutional nets for automated pulmonary nodule detection and classification. IEEE WACV 2018:673–681. https://doi.org/10.1101/189928 CrossRef

27.

Chen YP, Li JN, Xiao HX, Jin XJ, Yan SC, Feng JS (2017) Dual path networks. In: NIPS 2017

28.

Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. IEEE CVPR 2017:2261–2269. https://doi.org/10.1109/CVPR.2017.243 CrossRef

29.

Khosravan N, Bagci U (2018) S4ND: single-shot single-scale lung nodule detection. MICCAI 2018(11071):794–802. https://doi.org/10.1007/978-3-030-00934-2_88 CrossRef

30.

Zhu W, Vang YS, Huang Y, Xie X (2018) DeepEM: deep 3D ConvNets with EM for weakly supervised pulmonary nodule detection. MICCAI 2018(11071):812–820. https://doi.org/10.1007/978-3-030-00934-2_90 CrossRef

31.

Hu J, Shen L, Albanie S, Sun G, Wu EH (2018) Squeeze-and-excitation networks. In: IEEE CVPR 2018, pp 7132–7141. https://doi.org/10.1109/cvpr.2018.00745

32.

Zhu WT, Huang YF, Zeng L, Chen XM, Liu Y, Qian Z, Du N, Fan W, Xie XH (2018) AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys 46(2):576–589. https://doi.org/10.1002/mp.13300 CrossRefPubMed

Title: Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks
Authors: Li Gong
Shan Jiang
Zhiyong Yang
Guobin Zhang
Lu Wang
Publication date: 26-04-2019
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 11/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01979-1

Springer Professional

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 11/2019

Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair

Multiresolution vessel detection in magnetic particle imaging using wavelets and a Gaussian mixture model

Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks

Toward real-time rigid registration of intra-operative ultrasound with preoperative CT images for lumbar spinal fusion surgery

Pitfalls in interventional X-ray organ dose assessment—combined experimental and computational phantom study: application to prostatic artery embolization

Cervical cell recognition based on AGVF-Snake algorithm

Premium Partner