Abstract
Medical imaging and image analysis are important elements of modern diagnostic and treatment methods. Intelligent image processing, pattern recognition, and data analysis can be leveraged to introduce a new level of detection, segmentation, and, in general, understanding to medical image analysis. However, modern image analysis methods such as deep neural networks are often connected with significant computational complexity, slowing their adoption. Recent embedded systems such as the NVIDIA Jetson general-purpose GPUs became a viable platform for efficient execution of some computational models. This work analyzes the performance and time and energy costs of several neural models for medical image analysis on different kinds of NVIDIA Jetson modules. The experiments are performed with the lung X-ray medical images in connection with the COVID-19 disease.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al-Ayyoub, M., Abu-Dalo, A.M., Jararweh, Y., Jarrah, M., Al Sa’d, M.: A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. J. Supercomput. 71(8), 3149–3162 (2015). https://doi.org/10.1007/s11227-015-1431-y
Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42(11), 1–13 (2018)
Boveiri, H.R., Khayami, R., Javidan, R., Mehdizadeh, A.: Medical image registration using deep neural networks: a comprehensive review. Comput. Electr. Eng. 87, 106767 (2020)
Cass, S.: Nvidia makes it easy to embed AI: the Jetson nano packs a lot of machine-learning power into DIY projects - [Hands on]. IEEE Spectr. 57(7), 14–16 (2020). https://doi.org/10.1109/MSPEC.2020.9126102
Chowdhury, M.E., et al.: Can AI help in screening viral and covid-19 pneumonia? arXiv preprint arXiv:2003.13145 (2020)
Després, P., Jia, X.: A review of GPU-based medical image reconstruction. Physica Medica 42, 76–92 (2017)
Eklund, A., Dufort, P., Forsberg, D., LaConte, S.M.: Medical image processing on the GPU-past, present and future. Med. Image Anal. 17(8), 1073–1094 (2013)
Fluck, O., Vetter, C., Wein, W., Kamen, A., Preim, B., Westermann, R.: A survey of medical image registration on graphics hardware. Comput. Methods Prog. Biomed. 104(3), e45–e57 (2011)
Herbold, S.: Autorank: a python package for automated ranking of classifiers. J. Open Source Softw. 5(48), 2173 (2020). https://doi.org/10.21105/joss.02173
Huang, X., Sun, W., Tseng, T.L.B., Li, C., Qian, W.: Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput. Med. Imaging Graph. 74, 25–36 (2019)
Kalaiselvi, T., Sriramakrishnan, P., Somasundaram, K.: Survey of using GPU CUDA programming model in medical image analysis. Inform. Med. Unlocked 9, 133–144 (2017)
Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)
Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)
Kirk, D.: Nvidia CUDA software and GPU parallel computing architecture. In: Proceedings of the 6th International Symposium on Memory Management, ISMM 2007, pp. 103–104. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1296907.1296909
Kumar, K.K., Kumar, M.D., Samsonu, C., Krishna, K.V.: Role of convolutional neural networks for any real time image classification, recognition and analysis. Mater. Today Proc. (2021)
Luchies, A.C., Byram, B.C.: Deep neural networks for ultrasound beamforming. IEEE Trans. Med. Imaging 37(9), 2010–2021 (2018)
Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29(2), 102–127 (2019)
Maier, A., Syben, C., Lasser, T., Riess, C.: A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik 29(2), 86–101 (2019)
Pinochet, P., et al.: Evaluation of an automatic classification algorithm using convolutional neural networks in oncological positron emission tomography. Front. Med. 8, 117 (2021)
Pratx, G., Xing, L.: GPU computing in medical physics: a review. Med. Phys. 38(5), 2685–2697 (2011)
Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., Barfett, J.: Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 990–994. IEEE (2018)
Shams, R., Sadeghi, P., Kennedy, R., Hartley, R.: Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images. Comput. Methods Prog. Biomed. 99(2), 133–146 (2010)
Shams, R., Sadeghi, P., Kennedy, R.A., Hartley, R.I.: A survey of medical image registration on multicore and the GPU. IEEE Sig. Process. Mag. 27(2), 50–60 (2010)
Shi, L., Liu, W., Zhang, H., Xie, Y., Wang, D.: A survey of GPU-based medical image computing techniques. Quant. Imaging Med. Surg. 2(3), 188 (2012)
Yang, T.J., Chen, Y.H., Sze, V.: Designing energy-efficient convolutional neural networks using energy-aware pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5687–5695 (2017)
Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)
Acknowledgment
This work was supported from ERDF in project “A Research Platform focused on Industry 4.0 and Robotics in Ostrava”, reg. no. CZ.02.1.01/0.0/0.0/17_049/0008425, by the Technology Agency of the Czech Republic in the frame of the project no. TN01000024 “National Competence Center – Cybernetics and Artificial Intelligence”, and by the project of the Student Grant System no. SP2021/24, VSB - Technical University of Ostrava.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Krömer, P., Nowaková, J. (2022). Medical Image Analysis with NVIDIA Jetson GPU Modules. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-84910-8_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84909-2
Online ISBN: 978-3-030-84910-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)