An Investigation towards Challenges in medical image processing
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Abstract
Imaging is important in today's healthcare since it is used at every stage of the clinical process, from diagnosis and treatment planning to surgery and follow-up investigations. Large data volumes provide issues for medical image processing because most imaging modalities have gone completely digital with ever- increasing resolution. This work, address difficulties in the range of Kilo- to Terabytes related to bioimaging, virtual reality in medical visualisations, bioimage management, and neuroimaging. Algorithms for image processing and visualisation must be modified due to the growing volume of data. With the aid of graphical processing units, scalable algorithms and sophisticated parallelization strategies have been created. This publication provides a summary of them. Although these methods are managing the difficulty from Kilo to Terabyte, the Petabyte level is quickly approaching. Medical image processing is still an important area of study because of this.
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References
Antonelli, Michela, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett
A. Landman, Geert Litjens, et al. (2022). “The Medical Segmentation Decathlon.” Nature Communications 13 (1): 4128.
Bhattacharya, Sweta, Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Thippa Reddy Gadekallu, Siva Rama Krishnan S, Chiranji Lal Chowdhary, Mamoun Alazab, and Md Jalil Piran. (2021). “Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.” Sustainable Cities and Society 65 (February): 102589.
Budd, Samuel, Emma C. Robinson, and Bernhard Kainz. (2021). “A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis.” Medical Image Analysis 71 (July): 102062.
Dong, Nanqing, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, and Eric Xing. (2020). “Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small- Scale Data.” Applied Soft Computing 114 (January): 108074.
Haskins, Grant, Uwe Kruger, and Pingkun Yan. (2020). “Deep Learning in Medical Image Registration: A Survey.” Machine Vision and Applications 31 (1): 8.
Karimi, Davood, Haoran Dou, Simon K. Warfield, and Ali Gholipour. (2020). “Deep Learning with Noisy Labels: Exploring Techniques and Remedies in Medical Image Analysis.” Medical Image Analysis 65 (October): 101759.
Li, Jiangyun, Wenxuan Wang, Chen Chen, Tianxiang Zhang, Sen Zha, Jing Wang, and Hong Yu. (2022). “TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images.” arXiv [eess.IV]. arXiv. http://arxiv.org/abs/2201.12785.
Ma, K., S. He, G. Sinha, A. Ebadi, A. Florea, and S. Tremblay. (2023). “Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.” Sensors . https://www.mdpi.com/1424- 8220/23/19/8122.
Muckley, Matthew J., Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, et al. (2021). “Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction.” IEEE Transactions on Medical Imaging 40 (9): 2306–17.
Razzak, Muhammad Imran, Saeeda Naz, and Ahmad Zaib. (2018). “Deep Learning for Medical Image Processing: Overview, Challenges and the Future.” In Classification in BioApps: Automation of Decision Making, edited by Nilanjan Dey, Amira S. Ashour, and Surekha Borra, 323–50. Cham: Springer International Publishing.
Singh, Satya P., Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, and Balázs Gulyás. (2020). “3D Deep Learning on Medical Images: A Review.” Sensors 20 (18). https://doi.org/10.3390/s20185097.
Sultana, Maliha, Afrida Hossain, Fabiha Laila, Kazi Abu Taher, and Muhammad Nazrul Islam. (2020). “Towards Developing a Secure Medical Image Sharing System Based on Zero Trust Principles and Blockchain Technology.” BMC Medical Informatics and Decision Making 20 (1): 256.
Timmeren, Janita E. van, Davide Cester, Stephanie Tanadini-Lang, Hatem Alkadhi, and Bettina Baessler. (2020). “Radiomics in Medical Imaging-‘How-to’ Guide and Critical Reflection.” Insights into Imaging 11 (1): 91.
Valanarasu, Jeya Maria Jose, Vishwanath A. Sindagi, Ilker Hacihaliloglu, and Vishal M. Patel. (2020). “KiU-Net: Towards Accurate Segmentation of Biomedical Images Using Over-Complete Representations.” In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 363–73. Springer International Publishing.
Wang, Qiang, Yingkui Du, Huijie Fan, and Chi Ma. (2022). “Towards Collaborative Appearance and Semantic Adaptation for Medical Image Segmentation.” Neurocomputing 491 (June): 633–43.
Xie, Xiaozheng, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, and Shui Yu. (2021). “A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis.” Medical Image Analysis 69 (April): 101985.
Zhou, S. Kevin, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, and Ronald M. Summers. (2021). “A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises.” Proceedings of the IEEE. Institute of Electrical and Electronics Engineers 109 (5): 820–38.