Abstract
Positron emission tomography (PET) - computed tomography (CT) is a widely-accepted imaging modality for staging, diagnosis and treatment response monitoring of cancers. Deep learning based computer aided diagnosis systems have achieved high accuracy on tumor segmentation on PET-CT images in recent years. PET images can be used to detect functional structures such as tumors, whilst CT images provide complementary anatomical information. As for tumor detection using deep learning methods, multi-modality segmentation was verified to be effective. In this work, we propose a generative adversarial network (GAN) based augmentation method to synthesized multi-modality data pairs on PET and CT to improve the training of multi-modality segmentation method. Our novelty lies in creating a semantic label augmentation method to provide latent information that is suitable for the multi-modality synthesis. In addition, we set out a ‘Split U’ structure which can generate both PET-CT modalities from a latent input. Our experimental results demonstrated that the synthesized images generated by our method can be used to augment the training data for PET-CT segmentation.
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Kratochwil, C., Haberkorn, U., Giesel, F.L.: PET/CT for diagnostics and therapy stratification of lung cancer. Der Radiologe 50(8), 684–691 (2010)
Verma, B., Zakos, J.: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Trans. Inf Technol. Biomed. 5(1), 46–54 (2001)
Fan, J.-L., Zhao, F.: Two-dimensional Otsu’s curve thresholding segmentation method for gray-level images. Acta Electronica Sinica 35(4), 751 (2007)
Tang, J.: A color image segmentation algorithm based on region growing. In: 2010 2nd International Conference on Computer Engineering and Technology. IEEE (2010)
Hu, G.: Survey of recent volumetric medical image segmentation techniques. In: Biomedical Engineering. IntechOpen (2009)
Ker, J., et al.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2018)
Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD workshop (IIPhDW). IEEE (2018)
Wang, J., Perez, L.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw. Vis. Recogn. 11, 1–8 (2017)
Bi, L., Kim, J., Kumar, A., Feng, D., Fulham, M.: Synthesis of positron emission tomography (PET) Images via multi-channel generative adversarial networks (GANs). In: Cardoso, M.J., et al. (eds.) CMMI/SWITCH/RAMBO -2017. LNCS, vol. 10555, pp. 43–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67564-0_5
Peng, Y., et al. Deep multi-modality collaborative learning for distant metastases predication in PET-CT soft-tissue sarcoma studies. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2019)
Pisano, E.D., et al.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193 (1998). https://doi.org/10.1007/BF03178082
Pizer, S.M., et al.: Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the First Conference on Visualization in Biomedical Computing (1990)
Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Sign. Process. Syst. Sign. Image Video Technol. 38(1), 35–44 (2004). https://doi.org/10.1023/B:VLSI.0000028532.53893.82
Um, T.T., et al.: Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. arXiv preprint arXiv:1706.00527 (2017)
Taylor, L., Nitschke, G.: Improving deep learning using generic data augmentation. arXiv preprint arXiv:1708.06020 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Panchapagesan, S., et al.: Multi-task learning and weighted cross-entropy for DNN-based keyword spotting. In: INTERSPEECH (2016)
Vallières, M., et al.: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys. Med. Biol. 60(14), 5471 (2015)
Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7
Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition. IEEE (2010)
Kumar, A., et al.: Co-learning feature fusion maps from PET-CT images of lung cancer. IEEE Trans. Med. Imaging 39(1), 204–217 (2019)
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Cao, K., Bi, L., Feng, D., Kim, J. (2020). Improving PET-CT Image Segmentation via Deep Multi-modality Data Augmentation. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2020. Lecture Notes in Computer Science(), vol 12450. Springer, Cham. https://doi.org/10.1007/978-3-030-61598-7_14
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DOI: https://doi.org/10.1007/978-3-030-61598-7_14
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