Automated Medical Visualization Application of Supervised Learning to Clinical Diagnosis, Disease and Therapy Management.docx
Abstract
The rapid advancement and development in high performance computing, ultrafast computing, autonomous technologies and complexity of biomedical data for visualization and image guidance play a significant role in modern surgery to help surgeons perform their surgical procedures. Brain tumour diagnosis requires an enhanced, effective as well as accurate 3-D visualization system for navigation, reference, diagnosis as well as documentation. The automatic and effective 3-D high performance artificial intelligence-enabled medical visualization framework was designed and implemented using automated machine learning (AutoML) to take the advantage of complexity in the underlying datasets to help specialists in identifying the most appropriate regions of interest and their associated hyper parameters for optimizing performance, whilst simultaneously attempting to maximize the reliability of resulting predictions. C# and Compute Unified Device Architecture (CUDA) in Microsoft.NET environment in comparison side by side with visual basic studio was used for the implementation. The framework was evaluated for rendering processing speed with brain datasets obtained from the department of surgery, University of North Carolina, United States. Interestingly, our framework achieves 3-D visualization of the human brain, reliable enough to detect and locate possible brain tumor with high interactive speed and accuracy.
References
Adeshina, A.M., Hashim, R., Khalid, N.E.A., Abidin, S.Z.Z. 2011. Hardware-accelerated raycasting: Towards an
effective brain MRI visualization. J Comput 3, 36-42.
Adeshina, A.M., Hashim, R., Khalid, N.E.A., Abidin, S.Z.Z. 2012. Locating Abnormalities in Brain Blood
Vessels using Parallel Computing Architecture. Interdiscip Sci Comput Life Sci 4, 161-172.
Adeshina, A.M., Hashim, R., Khalid, N.E.A., Abidin, S.Z.Z. (2013). Multimodal 3-D Reconstruction of Human
Anatomical Structures using SurLens Visualization System. Interdiscip Sci Comput Life Sci 4, 161-172.
Adeshina AM, Hashim R, Khalid NEA (2014) CAHECA: Computer Aided Hepatocellular Carcinoma Therapy Planning. Interdiscip Sci Comput Life Sci 6:222–234.
Adeshina A.M., Hashim R. (2015) ConnectViz: Accelerated Approach for Brain Structural Connectivity using
Delaunay Triangulation. Interdisciplinary sciences: computational life sciences, pp 1–13
Adeshina, A.M., & Hashim, R. (2016). ConnectViz Accelerated Access forthe Human Brain
Structural connectivity using Delaunay triangulation Interdisciplinary Sciences: Computational Life Sciences pp. 55 – 65 .
Alseelawi, N., Hazim, H.T., TH, H., Alrikabi, S. (2022). A novel Method of Multimodal Medical Image Fusion
Based on Hybrid Approach of NSCT and DTCWT. International Journal of Online and Biomedical
Engineering. Vol. 18(3).
Bharathi, S., & Vasuki, A. (2018). Convert 2-D imageto3-D using Edge Information buffering
International Journal of Advances in Image Processing 2.Special Issue Special Issue on Communication Technology Interventions for Rural and Social Development (2018): 106-110.
Gholipour, A., Rollins, C.K., Velasco-Annis, C.,Akhondi-Asl, A., Clancy, S.,Limperopoulos,
C.,Estroff, J.A., Warfield, S.K. (2017). A Normative Spatiotemporal MRI Fetal Brain Atlas for Automatic Segmentation and Analysis of Early Brain Growth. Sci. Rep.7,-476.https://doi.org/10.1038/s41598-017.
Huff, D.T., Weisman, A.J., Jeraj, R. (2021). Interpretation and Visualization Techniques for Deep Learning.
Phys. Med. Biol 66.
Kanchan, A., Mathur, T. (2017). The latest trends in 2-D to 3-D Image Conversion Algorithm
View. International Research Journal of Engineering and Technology, 4(4), 3480-3484.
Kwang, L., Kyung, T., Park, J. Koo, Y. (2015). The GP-GPU based stream design Processor
for an Image Processing," IEEE, pp. 535-539.
McMahon, M., Wallace, O. (2015).What is visual imagination? Retrieved September 27
=.http://www.wisegeek.com/what-is-visual-imagery.htm.
Sang-Hyun, L., Dae-Won, P., Je-Pyong, J., and Kyung-II, M. (2014). Conversion of 2D Image to 3D Based on
Squeeze Function and Gradient Map. International Journal of Software Engineering and its Applications.
Vol. 8 (2) pp. 27-40.
Wang, Q., Chen, Z., Wang, Y., Qu, H. (2020). Applying Machine Learning Advances to Data Visualization. A
Survey on ML4Vis. arViz preprint.
Wierzbicki, R., Poawlowicz, M., Job, J., Balawender, R., Kostarczyk, W., Stanuch, M., Janc, K., Skalski, A.
(2022). Journal of Cancer Research and Clinical Oncology. 48., 237-243.
Yang, C., & Ranka, S. (2018). The Visual Explanation from Deep 3-D Convolutional Neural Networks for
Azheimer’s Disease Classification. AMIA Anna. Symp. Why. AMI. A Symp. 1571-1580.
