Abstract
The performance of a computer-aided automated diagnosis system of lung cancer from Computed Tomography (CT) volumetric images greatly depends on the accurate detection and segmentation of tumor regions. In this paper, we present Recurrent 3D-DenseUNet, a novel deep learning based architecture for volumetric lung tumor segmentation from CT scans. The proposed architecture consists of a 3D encoder block that learns to extract fine-grained spatial and coarse-grained temporal features, a recurrent block of multiple Convolutional Long Short-Term Memory (ConvLSTM) layers to extract fine-grained spatio-temporal information, and finally a 3D decoder block to reconstruct the desired volume segmentation masks from the latent feature space. The encoder and decoder blocks consist of several 3D-convolutional layers that are densely connected among themselves so that necessary feature aggregation can occur throughout the network. During prediction, we apply selective thresholding followed by morphological operation, on top of the network prediction, to better differentiate between tumorous and non-tumorous image-slices, which shows more promise than only thresholding-based approaches. We train and test our network on the NSCLC-Radiomics dataset of 300 patients, provided by The Cancer Imaging Archive (TCIA) for the 2018 IEEE VIP Cup. Moreover, we perform an extensive ablation study of different loss functions in practice for this task. The proposed network outperforms other state-of-the-art 3D segmentation architectures with an average dice score of 0.7228.
Source code available at https://github.com/muntakimrafi/TIA2020-Recurrent-3D-DenseUNet.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aerts, H.J., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5(1), 1–9 (2014)
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., Mougiakakou, S.: Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J. Biomed. Health Inform. 23(2), 714–722 (2018)
Chen, H., Dou, Q., Yu, L., Heng, P.A.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
del Ciello, A., Franchi, P., Contegiacomo, A., Cicchetti, G., Bonomo, L., Larici, A.R.: Missed lung cancer: when, where, and why? Diagn. Interv. Radiol. 23(2), 118 (2017)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Hashemi, S.R., Salehi, S.S.M., Erdogmus, D., Prabhu, S.P., Warfield, S.K., Gholipour, A.: Tversky as a loss function for highly unbalanced image segmentation using 3D fully convolutional deep networks. arXiv preprint arXiv:1803.11078 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hossain, S., Najeeb, S., Shahriyar, A., Abdullah, Z.R., Haque, M.A.: A pipeline for lung tumor detection and segmentation from CT scans using dilated convolutional neural networks. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1348–1352. IEEE (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lambin, P., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441–446 (2012)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes. arXiv preprint arXiv:1709.07330 (2017)
Liao, S., Gao, Y., Oto, A., Shen, D.: Representation learning: a unified deep learning framework for automatic prostate MR segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 254–261. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_32
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Mason, D.: Su-e-t-33: pydicom: an open source DICOM library. Med. Phys. 38(6Part10), 3493–3493 (2011)
Midthun, D.E.: Early detection of lung cancer. F1000Research 5 (2016)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Mohammadi, A., et al.: Lung cancer radiomics: highlights from the IEEE video and image processing cup 2018 student competition [sp competitions]. IEEE Signal Process. Mag. 36(1), 164–173 (2018)
Pang, S., Du, A., He, X., DÃez, J., Orgun, M.A.: Fast and accurate lung tumor spotting and segmentation for boundary delineation on CT slices in a coarse-to-fine framework. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1142, pp. 589–597. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36808-1_64
Pang, S., et al.: CTumorGAN: a unified framework for automatic computed tomography tumor segmentation. Eur. J. Nucl. Med. Mol. Imaging 47, 1–21 (2020)
Pataer, A., et al.: Histopathologic response criteria predict survival of patients with resected lung cancer after neoadjuvant chemotherapy. J. Thorac. Oncol. 7(5), 825–832 (2012)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)
Uzelaltinbulat, S., Ugur, B.: Lung tumor segmentation algorithm. Procedia Comput. Sci. 120, 140–147 (2017)
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kamal, U., Rafi, A.M., Hoque, R., Wu, J., Hasan, M.K. (2020). Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet. In: Petersen, J., et al. Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science(), vol 12502. Springer, Cham. https://doi.org/10.1007/978-3-030-62469-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-62469-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62468-2
Online ISBN: 978-3-030-62469-9
eBook Packages: Computer ScienceComputer Science (R0)