MMTDNN: Multi-View Massive Training Deep Neural Network for Segmentation and Detection of Abnormal Tissues in Medical Images
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Abstract
Purpose: Automated segmentation of abnormal tissues in medical images is considered as an essential part of those computer-aided detection and diagnosis systems which analyze medical images. However, automated segmentation of abnormalities is a challenging task due to the limitations of imaging technologies and complex structure of abnormalities, including low contrast between normal and abnormal tissues, shape diversity, appearance inhomogeneity, and the vague boundaries of abnormalities. Therefore, more intelligent segmentation techniques are required to tackle these challenges.
Materials and Methods: In this study, a method, which is called MMTDNN, is proposed to segment and detect medical image abnormalities. MMTDNN, as a multi-view learning machine, utilizes convolutional neural networks in a massive training strategy. Moreover, the proposed method has four phases of preprocessing, view generation, pixel-level segmentation, and post-processing. The International Symposium on Biomedical Imaging (ISBI)-2016 dataset is used for the evaluation of the proposed method.
Results: The performance of the proposed method has been evaluated on the task of skin lesion segmentation as one of the challenging applications of abnormal tissue segmentation. Both qualitative and quantitative results demonstrate outstanding performance. Meanwhile, the accuracy of 0.973, the Jaccard index of 0.876, and the Dice similarity coefficient of 0.931 have been achieved.
Conclusion: In conclusion, the experimental result demonstrates that the proposed method outperforms state-of-the-art methods of skin lesion segmentation.
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20- T. de Moor, A. Rodriguez-Ruiz, A. G. Mérida, R. Mann, and J. Teuwen, “Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network,” Feb. 2018.
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22- D. Gutman et al., “Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC),” pp. 3–7, 2016.
23- V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” Arxiv, pp. 1–28, 2016.
24- S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning, PMLR, 2015, vol. 37, pp. 448–456.
25- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout : A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., Vol. 15, no. 1, pp. 1929–1958, 2014.
26- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
27- N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979.
28- L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT’2010, Heidelberg: Physica-Verlag HD, 2010, pp. 177–186.
29- L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks,” IEEE Trans. Med. Imaging, vol. 36, no. 4, pp. 994–1004, Apr. 2017.
30- L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng., vol. 64, no. 9, pp. 2065–2074, Sep. 2017.
2-H. Khastavaneh and H. Ebrahimpour-Komleh, “Representation Learning Techniques: An Overview,” in Data Science: From Research to Application, Springer, 2020.
3- Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 35, No. 8, pp. 1798–1828, Aug. 2013.
4- K. Suzuki, “Overview of deep learning in medical imaging,” Radiol. Phys. Technol., Vol. 10, No. 3, pp. 257–273, 2017.
5- K. Suzuki, S. G. Armato, F. Li, S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography,” Med. Phys., vol. 30, no. 7, pp. 1602–1617, Jun. 2003.
6- H. Khastavaneh and H. Haron, “A Conceptual Model for Segmentation of Multiple Scleroses Lesions in Magnetic Resonance Images Using Massive Training Artificial Neural Network,” in 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, 2014, vol. 2015-Septe, pp. 273–278.
7- H. Khastavaneh and H. Ebrahimpour-Komleh, “Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images.,” J. Biomed. Phys. Eng., vol. 7, no. 2, pp. 155–162, Jun. 2017.
8- K. Suzuki, H. Yoshida, J. Näppi, S. G. Armato, and A. H. Dachman, “Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography,” Med. Phys., vol. 35, no. 2, pp. 694–703, Jan. 2008.
9- J.-W. Xu and K. Suzuki, “Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography,” Med. Phys., vol. 38, no. 4, pp. 1888–1902, Mar. 2011.
10- K. Suzuki, H. Yoshida, J. Näppi, and A. H. Dachman, “Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes,” Med. Phys., vol. 33, no. 10, pp. 3814–3824, Sep. 2006.
11- K. Suzuki, Jun Zhang, and Jianwu Xu, “Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography,” IEEE Trans. Med. Imaging, vol. 29, no. 11, pp. 1907–1917, Nov. 2010.
12- K. Suzuki, H. Abe, H. MacMahon, and K. Doi, “Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN),” IEEE Trans. Med. Imaging, vol. 25, no. 4, pp. 406–416, Apr. 2006.
13- S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240–1251, May 2016.
14- M. Havaei et al., “Brain tumor segmentation with Deep Neural Networks,” Med. Image Anal., vol. 35, pp. 18–31, Jan. 2017.
15- K. H. Cha, L. Hadjiiski, R. K. Samala, H. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Med. Phys., vol. 43, no. 4, pp. 1882–1896, Mar. 2016.
16- M. H. Jafari, E. Nasr-Esfahani, N. Karimi, S. M. R. Soroushmehr, S. Samavi, and K. Najarian, “Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma,” Int. J. Comput. Assist. Radiol. Surg., vol. 12, no. 6, pp. 1021–1030, Jun. 2017.
17- Y. Yuan, M. Chao, and Y.-C. Lo, “Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance,” IEEE Trans. Med. Imaging, vol. 36, no. 9, pp. 1876–1886, Sep. 2017.
18- T. Brosch, L. Y. W. Tang, Y. Yoo, D. K. B. Li, A. Traboulsee, and R. Tam, “Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1229–1239, May 2016.
19- H. Li et al., “Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images,” Neuroimage, vol. 183, no. June, pp. 650–665, Dec. 2018.
20- T. de Moor, A. Rodriguez-Ruiz, A. G. Mérida, R. Mann, and J. Teuwen, “Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network,” Feb. 2018.
21- H. Khastavaneh and H. Ebrahimpour-Komleh, “On Multi-view Interpretation of Convolutional Neural Networks,” in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019, pp. 587–591.
22- D. Gutman et al., “Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC),” pp. 3–7, 2016.
23- V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” Arxiv, pp. 1–28, 2016.
24- S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning, PMLR, 2015, vol. 37, pp. 448–456.
25- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout : A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., Vol. 15, no. 1, pp. 1929–1958, 2014.
26- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
27- N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979.
28- L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT’2010, Heidelberg: Physica-Verlag HD, 2010, pp. 177–186.
29- L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks,” IEEE Trans. Med. Imaging, vol. 36, no. 4, pp. 994–1004, Apr. 2017.
30- L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham, and D. Feng, “Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks,” IEEE Trans. Biomed. Eng., vol. 64, no. 9, pp. 2065–2074, Sep. 2017.
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| Issue | Vol 7 No 1 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v7i1.2722 | |
| Keywords | ||
| Medical Imaging Abnormal Tissues Segmentation Convolutional Neural Networks Artificial Neural Networks Multi-View Learning Multi-View Massive Training Deep Neural Network | ||
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How to Cite
1.
Homayoun H, Ebrahimpour-Komleh H. MMTDNN: Multi-View Massive Training Deep Neural Network for Segmentation and Detection of Abnormal Tissues in Medical Images. Frontiers Biomed Technol. 2020;7(1):22-32.
