Abstract
Pulmonary nodule detection and segmentation are two important works for early diagnosis and treatment of lung cancer. The work of detection is to locate pulmonary nodules in a given chest CT scan, and the segmentation aims at extracting all the voxels from a CT scan within each nodule’s space. This paper propose a novel framework to process both nodule detection and segmentation integrately, which is implemented as the combination of SLIC supervoxel segmentation and CNN classification. The learning of CNN just require weakly labeled data annotations, where only a single coordinate is provided for each annotated nodule as the ground truth. The CNN architecture is designed as a 3D multi-level framework, which is able to comprehensively recognize nodules with variant sizes and shapes. Experiments on the dataset of LUNA16 challenge expressed prominent detecting performance, demonstrating the necessity and efficiency of 3D CNN architecture and multi-level framework for computer-aided detection of pulmonary nodules. Meanwhile the evaluation of segmentation presented impressive performance, producing elegant shapes of real nodules, which proves the great efficiency of SLIC technique.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282
Anirudh R, Thiagarajan JJ, Bremer T, Kim H (2016) Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. In: Medical imaging 2016: computer-aided diagnosis, international society for optics and photonics, vol 9785, p 978532
Dobrenkii A, Kuleev R, Khan A, Rivera AR, Khattak AM (2017) Large residual multiple view 3D CNN for false positive reduction in pulmonary nodule detection. In: Computational intelligence in bioinformatics and computational biology (CIBCB), 2017 IEEE conference. IEEE, pp 1–6
Dou Q, Chen H, Yu L, Qin J, Heng PA (2017) Multilevel contextual 3-D CNNS for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558–1567
Fakoor R, Ladhak F, Nazi A, Huber M (2013) Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the international conference on machine learning, vol 28
Feng X, Yang J, Laine AF, Angelini ED (2017) Discriminative localization in CNNS for weakly-supervised segmentation of pulmonary nodules. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 568–576
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on machine learning, pp 448–456
Jacobs C, van Rikxoort EM, Twellmann T, Scholten ET, de Jong PA, Kuhnigk JM, Oudkerk M, de Koning HJ, Prokop M, Schaefer-Prokop C et al (2014) Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal 18(2):374–384
Kuhnigk JM, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen HO (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic ct scans. IEEE Trans Med Imaging 25(4):417–434
Lin TY, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 2980–2988
Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406
Mingliang X, Pei L, Mingyuan L, Hao F, Hongling Z, Bing Z, Yusong L, Liwei Z (2016) Medical image denoising by parallel non-local means. Neurocomputing 195:117–122
Murphy K, van Ginneken B, Schilham AM, De Hoop B, Gietema H, Prokop M (2009) A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal 13(5):757–770
Niemeijer M, Loog M, Abramoff MD, Viergever MA, Prokop M, van Ginneken B (2011) On combining computer-aided detection systems. IEEE Trans Med Imaging 30(2):215–223
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, pp 246–253
Ramaswamy S, Truong K (2017) Pulmonary nodule classification with convolutional neural networks. http://cs231n.stanford.edu/reports/2016/pdfs/324_Report.pdf
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241
Setio AA, Jacobs C, Gelderblom J, Ginneken B (2015) Automatic detection of large pulmonary solid nodules in thoracic CT images. Med Phys 42(10):5642–5653
Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sánchez 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
Shao L, Wu D, Li X (2014) Learning deep and wide: a spectral method for learning deep networks. IEEE Trans Neural Netw Learn Syst 25(12):2303–2308
Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196–1206
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
Tang W, Wang Y, He W (2016) An image segmentation algorithm based on improved multiscale random field model in wavelet domain. J Ambient Intell Humaniz Comput 7(2):221–228
Van Ginneken B, Setio AA, Jacobs C, Ciompi F (2015) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: Biomedical imaging (ISBI), 2015 IEEE 12th international symposium. IEEE, pp 286–289
Wu D, Pigou L, Kindermans PJ, Le NDH, Shao L, Dambre J, Odobez JM (2016) Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Trans Pattern Anal Mach Intell 38(8):1583–1597
Yang H, Yu H, Wang G (2016) Deep learning for the classification of lung nodules. arXiv:1611.06651
Acknowledgements
This research is funded by the Zhejiang Provincial Natural Science Foundation of China under Grant no. LY18F020034, the Natural Science Foundation of China under Grant no. 61801428, no. 61872317. The work of this paper is also supported by Shanghai Pulmonary Hospital, who provided a vast annotated pulmonary dataset to help our experiments.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Feng, Y., Hao, P., Zhang, P. et al. Supervoxel based weakly-supervised multi-level 3D CNNs for lung nodule detection and segmentation. J Ambient Intell Human Comput 14, 14817–14827 (2023). https://doi.org/10.1007/s12652-018-01170-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-01170-5