A Comparison of Deep Learning and Pharmacokinetic Model Selection Methods in Segmentation of High-Grade Glioma
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Abstract
Purpose: Glioma tumor segmentation is an essential step in clinical decision making. Recently, computer-aided methods have been widely used for rapid and accurate delineation of the tumor regions. Methods based on image feature extraction can be used as fast methods, while segmentation based on the physiology and pharmacokinetic of the tissues is more accurate. This study aims to compare the performance of tumor segmentation based on these two different methods.
Materials and Methods: Nested Model Selection (NMS) based on Extended-Toft’s model was applied to 190 Dynamic Contrast-Enhanced MRI (DCE-MRI) slices acquired from 25 Glioblastoma Multiforme (GBM) patients in 70 time-points. A model with three pharmacokinetic parameters, Model 3, is usually assigned to tumor voxel based on the time-contrast concentration signal. We utilized Deep-Net as a CNN network, based on Deeplabv3+ and layers of pre-trained resnet18, which has been trained with 17288 T1-Contrast MRI slices with HGG brain tumor to predict the tumor region in our 190 DCE MRI T1 images. The NMS-based physiological tumor segmentation was considered as a reference to compare the results of tumor segmentation by Deep-Net. Dice, Jaccard, and overlay similarity coefficients were used to evaluate the tumor segmentation accuracy and reliability of the Deep tumor segmentation method.
Results: The results showed a relatively high similarity coefficient (Dice coefficient: 0.73±0.15, Jaccard coefficient: 0.66±0.17, and overlay coefficient: 0.71±0.15) between deep learning tumor segmentation and the tumor region identified by the NMS method. The results indicate that the deep learning methods may be used as accurate and robust tumor segmentation.
Conclusion: Deep learning-based segmentation can play a significant role to increase the segmentation accuracy in clinical application, if their training process is completely automatic and independent from human error.
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27- G. Kim, "Brain tumor segmentation using deep fully convolutional neural networks," in International MICCAI Brainlesion Workshop, 2017: Springer, pp. 344-357.
28- K. Pawar, Z. Chen, N. J. Shah, and G. Egan, "Residual encoder and convolutional decoder neural network for glioma segmentation," in International MICCAI Brainlesion Workshop, 2017: Springer, pp. 263-273.
29- R. Thillaikkarasi and S. Saravanan, "An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM," Journal of medical systems, vol. 43, no. 4, p. 84, 2019.
30- M. W. Nadeem et al., "Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges," Brain Sciences, vol. 10, no. 2, p. 118, 2020.
31- R. A. Zeineldin, M. E. Karar, J. Coburger, C. R. Wirtz, and O. Burgert, "DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images," International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 6, pp. 909-920, 2020/06/01 2020, doi: 10.1007/s11548-020-02186-z.
32- K. Kamnitsas et al., "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation," Medical image analysis, vol. 36, pp. 61-78, 2017.
33- P. S. Tofts et al., "Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols," Journal of Magnetic Resonance Imaging, vol. 10, no. 3, pp. 223-232, 1999, doi: 10.1002/(sici)1522-2586(199909)10:3<223: aid-jmri2>3.0.co;2-s.
34- H. Bagher-Ebadian et al., "Model Selection for DCE-T1 Studies in Glioblastoma," Magn Reson Med, vol. 68, no. 1, pp. 241-251, 2012, doi: 10.1002/mrm.23211.
35- J. R. Ewing, R. Elmghirbi, and T. Nagaraja, "Chapter 20 - Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Brain Tumors," in Nervous System Drug Delivery, R. R. Lonser, M. Sarntinoranont, and K. Bankiewicz Eds.: Academic Press, 2019, pp. 405-428.
36- J. R. Ewing et al., "Model selection in magnetic resonance imaging measurements of vascular permeability: Gadomer in a 9L model of rat cerebral tumor.," J Cereb Blood Flow Metab, vol. 26, no. 3, pp. 310-320, 2006
37- D. A. N.V., K. A. Alireza, E. J. R., W. Ning, C. I. J., and B. E. Hassan, "An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies," NMR in biomedicine, vol. 30, no. 5, p. e3682, 2017, doi: doi:10.1002/nbm.3682.
38- H. Bagher-Ebadian, A. Dehkordi, and J. Ewing, "SU-F-I-26: Maximum Likelihood and Nested Model Selection Techniques for Pharmacokinetic Analysis of Dynamic Contrast Enhanced MRI in Patients with Glioblastoma Tumors," Medical Physics, vol. 43, no. 6, pp. 3392-3392, 2016, doi: doi:http://dx.doi.org/10.1118/1.4955854.
39- L. R. Dice, "Measures of the Amount of Ecologic Association Between Species," Ecology, vol. 26, no. 3, pp. 297-302, 1945, doi: https://doi.org/10.2307/1932409.
40- T. J. Sørensen, A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. København: I kommission hos E. Munksgaard (in English), 1948.
41- P. Jaccard, "THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1," New Phytologist, vol. 11, no. 2, pp. 37-50, 1912, doi: https://doi.org/10.1111/j.1469-8137.1912.tb05611.x.
42- M. Vijaymeena and K. Kavitha, "A survey on similarity measures in text mining," Machine Learning and Applications: An International Journal, vol. 3, no. 2, pp. 19-28, 2016.
43- A. N. Dehkordi, "The Effect of the Bias in the Contrast Agent Concentration Measurement on the Estimated Pharmacokinetic Parameters in Brain Dynamic Contrast Enhanced Magnetic Resonance Imaging Studies," Iranian Journal of Medical Physics, pp. -, 2019, doi: 10.22038/ijmp.2019.41400.1598.
44- A. N. V. Dehkordi et al., "Dynamic Contrast Enhanced MR Estimation of Pharmacokinetic Parameters of Glioblastoma Multiforme Tumors: A Comparison Study for Peritumoral and Global Arterial Input Functions Using Model Selection Technique," Neuro-Oncology, vol. 17, no. 5, pp. 165-166, November 1, 2015 2015, doi: 10.1093/neuonc/nov225.52.
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2- S. A. Naji, R. Zainuddin, and H. A. Jalab, "Skin segmentation based on multi pixel color clustering models," Digital Signal Processing, vol. 22, no. 6, pp. 933-940, 2012/12/01/ 2012, doi: https://doi.org/10.1016/j.dsp.2012.05.004.
3- S. Saravanan, R. Karthigaivel, and V. Magudeeswaran, "A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model," Journal of Ambient Intelligence and Humanized Computing, 2020/03/29 2020, doi: 10.1007/s12652-020-01875-6.
4- M. Agn et al., "A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning," Medical Image Analysis, vol. 54, pp. 220-237, 2019/05/01/ 2019, doi: https://doi.org/10.1016/j.media.2019.03.005.
5- B. Leena and A. Jayanthi, "Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification," International Journal of Imaging Systems and Technology, vol. 30, no. 4, pp. 874-898, 2020, doi: https://doi.org/10.1002/ima.22420.
6- A.Rajendran and R. Dhanasekaran, "Fuzzy Clustering and Deformable Model for Tumor Segmentation on MRI Brain Image: A Combined Approach," Procedia Engineering, vol. 30, pp. 327-333, 2012/01/01/ 2012, doi: https://doi.org/10.1016/j.proeng.2012.01.868.
7- A. Bhandari, J. Koppen, and M. Agzarian, "Convolutional neural networks for brain tumour segmentation," Insights into Imaging, vol. 11, no. 1, p. 77, 2020/06/08 2020, doi: 10.1186/s13244-020-00869-4.
8- Y. Wang, C. Li, T. Zhu, and C. Yu, "A Deep Learning Algorithm for Fully Automatic Brain Tumor Segmentation," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019: IEEE, pp. 1-5.
9- G. Wang, W. Li, S. Ourselin, and T. Vercauteren, "Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation," (in English), Frontiers in Computational Neuroscience, Original Research vol. 13, no. 56, 2019-August-13 2019, doi: 10.3389/fncom.2019.00056.
10- M. H. Hesamian, W. Jia, X. He, and P. Kennedy, "Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges," Journal of Digital Imaging, journal article vol. 32, no. 4, pp. 582-596, August 01 2019, doi: 10.1007/s10278-019-00227-x.
11- M. Havaei et al., "Brain tumor segmentation with Deep Neural Networks," Medical Image Analysis, vol. 35, pp. 18-31, 2017/01/01/ 2017, doi: https://doi.org/10.1016/j.media.2016.05.004.
12- A. Işın, C. Direkoğlu, and M. Şah, "Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods," Procedia Computer Science, vol. 102, pp. 317-324, 2016/01/01/ 2016, doi: https://doi.org/10.1016/j.procs.2016.09.407.
13- H. Dong, G. Yang, F. Liu, Y. Mo, and Y. Guo, "Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks," in annual conference on medical image understanding and analysis, 2017: Springer, pp. 506-517.
14- F. Isensee and K. Maier-Hein, "An Attempt at Beating the 3D U-Net. 2019," arXiv preprint arXiv:1908.02182.
15- A. N. Dehkordi and S. Koohestani, "The Influence of Signal to Noise Ratio on the Pharmacokinetic Analysis in DCE-MRI Studies," Frontiers in Biomedical Technologies, 2019.
16- M. Zhao et al., "Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging," (in eng), Oncol Lett, vol. 14, no. 5, pp. 5418-5426, 2017, doi: 10.3892/ol.2017.6895.
17- S. Bakas, H. Akbari, and A. Sotiras, "Segmentation labels for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive," ed, 2017.
18- R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018/08/01 2018, doi: 10.1007/s13244-018-0639-9.
19- C. Wachinger, M. Reuter, and T. Klein, "DeepNAT: Deep convolutional neural network for segmenting neuroanatomy," (in eng), NeuroImage, vol. 170, pp. 434-445, 2018, doi: 10.1016/j.neuroimage.2017.02.035.
20- J. Amin, M. Sharif, M. Raza, and M. Yasmin, "Detection of Brain Tumor based on Features Fusion and Machine Learning," Journal of Ambient Intelligence and Humanized Computing, 2018/11/01 2018, doi: 10.1007/s12652-018-1092-9.
21- X. Kong, G. Sun, Q. Wu, J. Liu, and F. Lin, "Hybrid pyramid u-net model for brain tumor segmentation," in International conference on intelligent information processing, 2018: Springer, pp. 346-355.
22- M. Lopez and J. Ventura, "Dilated Convolutions for Brain Tumor Segmentation in MRI Scans," in BrainLes@MICCAI, 2017.
23- F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 July 2017 2017, pp. 1800-1807, doi: 10.1109/CVPR.2017.195.
24- M. Marcinkiewicz, J. Nalepa, P. R. Lorenzo, W. Dudzik, and G. Mrukwa, "Segmenting brain tumors from MRI using cascaded multi-modal U-Nets," in International MICCAI Brainlesion Workshop, 2018: Springer, pp. 13-24.
25- M. Rezaei, H. Yang, K. Harmuth, and C. Meinel, "Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation," in 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019: IEEE, pp. 1836-1845.
26- R. Pourreza, Y. Zhuge, H. Ning, and R. Miller, "Brain tumor segmentation in mri scans using deeply-supervised neural networks," in International MICCAI Brainlesion Workshop, 2017: Springer, pp. 320-331.
27- G. Kim, "Brain tumor segmentation using deep fully convolutional neural networks," in International MICCAI Brainlesion Workshop, 2017: Springer, pp. 344-357.
28- K. Pawar, Z. Chen, N. J. Shah, and G. Egan, "Residual encoder and convolutional decoder neural network for glioma segmentation," in International MICCAI Brainlesion Workshop, 2017: Springer, pp. 263-273.
29- R. Thillaikkarasi and S. Saravanan, "An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM," Journal of medical systems, vol. 43, no. 4, p. 84, 2019.
30- M. W. Nadeem et al., "Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges," Brain Sciences, vol. 10, no. 2, p. 118, 2020.
31- R. A. Zeineldin, M. E. Karar, J. Coburger, C. R. Wirtz, and O. Burgert, "DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images," International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 6, pp. 909-920, 2020/06/01 2020, doi: 10.1007/s11548-020-02186-z.
32- K. Kamnitsas et al., "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation," Medical image analysis, vol. 36, pp. 61-78, 2017.
33- P. S. Tofts et al., "Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols," Journal of Magnetic Resonance Imaging, vol. 10, no. 3, pp. 223-232, 1999, doi: 10.1002/(sici)1522-2586(199909)10:3<223: aid-jmri2>3.0.co;2-s.
34- H. Bagher-Ebadian et al., "Model Selection for DCE-T1 Studies in Glioblastoma," Magn Reson Med, vol. 68, no. 1, pp. 241-251, 2012, doi: 10.1002/mrm.23211.
35- J. R. Ewing, R. Elmghirbi, and T. Nagaraja, "Chapter 20 - Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Brain Tumors," in Nervous System Drug Delivery, R. R. Lonser, M. Sarntinoranont, and K. Bankiewicz Eds.: Academic Press, 2019, pp. 405-428.
36- J. R. Ewing et al., "Model selection in magnetic resonance imaging measurements of vascular permeability: Gadomer in a 9L model of rat cerebral tumor.," J Cereb Blood Flow Metab, vol. 26, no. 3, pp. 310-320, 2006
37- D. A. N.V., K. A. Alireza, E. J. R., W. Ning, C. I. J., and B. E. Hassan, "An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies," NMR in biomedicine, vol. 30, no. 5, p. e3682, 2017, doi: doi:10.1002/nbm.3682.
38- H. Bagher-Ebadian, A. Dehkordi, and J. Ewing, "SU-F-I-26: Maximum Likelihood and Nested Model Selection Techniques for Pharmacokinetic Analysis of Dynamic Contrast Enhanced MRI in Patients with Glioblastoma Tumors," Medical Physics, vol. 43, no. 6, pp. 3392-3392, 2016, doi: doi:http://dx.doi.org/10.1118/1.4955854.
39- L. R. Dice, "Measures of the Amount of Ecologic Association Between Species," Ecology, vol. 26, no. 3, pp. 297-302, 1945, doi: https://doi.org/10.2307/1932409.
40- T. J. Sørensen, A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. København: I kommission hos E. Munksgaard (in English), 1948.
41- P. Jaccard, "THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1," New Phytologist, vol. 11, no. 2, pp. 37-50, 1912, doi: https://doi.org/10.1111/j.1469-8137.1912.tb05611.x.
42- M. Vijaymeena and K. Kavitha, "A survey on similarity measures in text mining," Machine Learning and Applications: An International Journal, vol. 3, no. 2, pp. 19-28, 2016.
43- A. N. Dehkordi, "The Effect of the Bias in the Contrast Agent Concentration Measurement on the Estimated Pharmacokinetic Parameters in Brain Dynamic Contrast Enhanced Magnetic Resonance Imaging Studies," Iranian Journal of Medical Physics, pp. -, 2019, doi: 10.22038/ijmp.2019.41400.1598.
44- A. N. V. Dehkordi et al., "Dynamic Contrast Enhanced MR Estimation of Pharmacokinetic Parameters of Glioblastoma Multiforme Tumors: A Comparison Study for Peritumoral and Global Arterial Input Functions Using Model Selection Technique," Neuro-Oncology, vol. 17, no. 5, pp. 165-166, November 1, 2015 2015, doi: 10.1093/neuonc/nov225.52.
45- J. R. Ewing and H. Bagher-Ebadian, "Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications," (in eng), NMR in biomedicine, vol. 26, no. 8, pp. 1028-1041, 2013, doi: 10.1002/nbm.2996.
| Files | ||
| Issue | Vol 8 No 1 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v8i1.5858 | |
| Keywords | ||
| Pharmacokinetic Analysis Nested Model Selection T1-Weighted Contrast Enhanced Magnetic Resonance Imaging Tumor Segmentation Deep Learning-Based Algorithm | ||
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How to Cite
1.
Dehkordi A, Sina S, Khodadadi F. A Comparison of Deep Learning and Pharmacokinetic Model Selection Methods in Segmentation of High-Grade Glioma. Frontiers Biomed Technol. 2021;8(1):50-60.
