İnmenin Beyin Radyolojik BT Görüntülerinden Bilgisayar Destekli Derin Öğrenmeye Dayalı Değerlendirilmesi

Öz

Çalışmanın amacı, MATLAB 2019b arayüzünde Derin Öğrenme modelleri ile inme hastalarının beyin BT'lerinden Görüntü İşleme kullanarak anormal alan(lar)ı tespit etmek ve hastalarda beyin dokularındaki inme değişikliklerini doğru bir şekilde değerlendirmektir. TOBB ETÜ ve Yıldırım Beyazıt Üniversitesi Hastanelerinden 25-75 yaş aralığında 1000 hasta (500 inme şüphelisi, 500 sağlıklı katılımcı) etik kurul sertifikasına göre seçilmiştir. Bu çalışma için hastaların görüntü verilerinden doğruluğu artırmak ve fazlalığı ortadan kaldırmak için sadece lateral ve 4. ventrikül BT görüntüleri kullanıldı. İlk olarak bu görüntüler Görüntü İşleme yöntemleri (Görüntü Toplama, Ön İşleme, Eşikleme, Segmentasyon, Morfolojik İşlemler vb.) ile işlenmiştir. Bu yöntemlerden sonra elde edilen lateral ventrikül görüntüsü 6 spesifik alana bölündü ve 4. ventrikül görüntüsü otomatik bilgisayarlı Alberta Stroke Skorlama gibi sırasıyla 14 spesifik alana bölündü. 1000 görüntü için, belirli sınıf adlarıyla (sağlıklı ve felçli olarak) toplam 20x1000=20000 adet BT alt görüntüsü elde edilmiş ve Yapay Zeka (AI) ve Derin Öğrenme (DL) modellerinin (Levenberg ile optimize edilmiş YSA) girdisi olarak kullanılmıştır. Marquardt yöntemi ve KSA). Bu yaklaşım, doktorlara sonuçlarını bir karar destek sistemi ile desteklemeleri, teşhis süresini hızlandırmaları ve olası yanlış teşhis oranlarını azaltmaları için önemli bir şans verebilir.

Anahtar Kelimeler

• Yapay Zeka
• Derin Öğrenme
• İnme Tanılama
• Bilgisayar Destekli Tanı
• İnme Önleme

Computer Aided Deep Learning Based Assessment of Stroke From Brain Radiological CT Images

Öz

The aim of the study is to detect the abnormal area(s) from brain CTs of stroke patients using Image Processing and to accurately evaluate the stroke changes in brain tissues among patients with Deep Learning models in MATLAB 2019b interface. 1000 patients (500 stroke suspected, 500 healthy participants) were chosen between 25 and 75 age ranges from TOBB ETU and Yıldırım Beyazıt University Hospitals according to the ethics committee certificate. For this study, for increasing the accuracy and eliminating the redundancy, from the image data of the patients, only lateral and 4th ventricle CT images were used. Firstly, these images were processed via Image Processing methods (Image Acquisition, Preprocessing, Thresholding, Segmentation, Morphological Operations etc.). After these methods, the resulted lateral ventricle image was split into 6 specific areas and 4th ventricle image was split into 14 specific areas like automated computerized Alberta Stroke Scoring, respectively. For 1000 images, totally 20x1000=20000 pieces of CT subimages were obtained with the specific class names (as healthy and stroke) and were used as the input of Artificial Intelligence (AI) and Deep Learning (DL) models (optimized ANN with Levenberg-Marquardt method and CNN). This approach can give an important chance to the doctors for supporting their results with a decision support system, speeding up the diagnosis time and also decreasing the possible rate of misdiagnosis.

Anahtar Kelimeler

• Artificial Intelligence
• Deep Learning
• Stroke Diagnosis
• CAD
• Stroke Prevention

Kaynakça

1. [1] Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., Kitai, T., “Artificial Intelligence in Precision Cardiovascular Medicine”, Journal of American College of Cardiology, 69(21):2657-2664, (2017).
2. [2] Bryan, R.N., Levy, L.M., Whitlow, W.D., Killian, J.M., Preziosi, T.J., Rosairo, J.A., “Diagnosis of acute cerebral infarction: comparison of CT and MR imaging”, American Journal of Neuroradiology, 12(4):611-620, (1991).
3. [3] Tyan, Y.S., Wu, M.C., Chin, C.L., Kuo, Y.L., Lee, M.S., Chang, H.Y., “Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method”, Journal of Biomedical Imaging, 19:9, (2015).
4. [4] Pexman, J.H.W., Barber, P.A., Hill, M.D., Sevick, R.J., Demchuk, A.M., Hudon, M.E., Hu, W.Y., Buchan, A.M., “Use of The Alberta Stroke Program Early CT Score (ASPECTS) For Assessing CT Scans In Patients With Acute Stroke”, American Journal Of Neuroradiology, 22(8):1534-1542, (2001).
5. [5] Yoo, A.J., Zaidat, O.O., Chaudhry, Z.A., Berkhemer, O.A., Gonzales, R.G., Goyal, M., Demchuk, A.M., Menon, B.K., Mualem, E., Ueda, D., Buell, H., Sit, S.P., Bose, A., “Impact Of Pretreatment Noncontrast CT Alberta Stroke Program Early CT Score On Clinical Outcome After Intra-Arterial Stroke Therapy”, Stroke, 45(3):746-751, (2014).
6. [6] Demchuk, A.M., Hill, M.D., Barber, P.A., Silver, B., Patel, S.C., Levine, S.R., “Importance Of Early Ischemic Computed Tomography Changes Using ASPECTS In Ninds Rtpa Stroke Study”, Stroke, 36(10):2110-2115, (2014).
7. [7] Aviv, R.I., Mandelcorn, J., Chakraborty, S., Gladstone, D., Malham, S., Tomlinson, G., Fox, A.J., Symons, S., “Alberta Stroke Program Early CT Scoring Of CT Perfusion In Early Stroke Visualization and Assessment”, American Journal Of Neuroradiology, 28 (10):1975-1980, (2007).
8. [8] Iihara, K., Nishimura, K. Kada, A., Nakagawara, J., Ogasawara, K., Ono, J., Shiokawa, Y., Aruga, T., Miyachi, S., Nagata, I., Toyoda, K., Matsuda, S., Miyamoto, Y., Suzuki, A., Ishikawa, K.B., Kataoka, H., Nakamura, F., Kamitani, S., “Effects Of Comprehensive Stroke Care Capabilities On In-Hospital Mortality Of Patients With Ishemic And Hemorrhagic Stroke: J-ASPECT Study”, PLOS ONE, 9 (5), e96819, (2014).

9. [9] Herweh, C., Ringleb, P.A., Rauch, G., Gerry, S., Behrens, L., Möhlenbruch, M., Gotfort, R., Richter, D., Schieber, Nagel, S., “Performance Of e-ASPECTS Softare In Comparison To That Of Stroke Physicians On Assessing CT Scans Of Acute Ischemic Stroke Patients”, International Journal Of Stroke, 11 (4):438-445, (2016).
10. [10] Puetz, V, Dzialowski, M., Hill, D., Demchuk, M., “The Alberta Stroke Program Early CT Score in Clinical Practice: What have We Learned”, International Journal of Stroke, 4(5):354-364, (2019).
11. [11] Ural, B., Özışık, P., Hardalaç, F., “An Improved Computer Based Diagnosis System For Early Detection Of Abnormal Lesions In The Brain Tissues With Using Magnetic Resonance and Computerized Tomography Images”, Multimedia Tool and Applications, (2019).
12. [12] Ural, B., “A Computer-Based Brain Tumor Detection Approach with Advanced Image Processing and Probabilistic Neural Network Methods”, Journal of Medical and Biological Engineering, 38(6):867-879, (2018).
13. [13] Jeena, R.S., Kumar, S., “A comparative analysis of MRI and CT brain images for stroke diagnosis”, 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy, Kanjirapally, 1-5, (2013).
14. [14] Rajini, N.H., Bhavani, R., “Computer Aided Detection of Ischemic Stroke Using Segmentation and Texture Features”, Measurement, 46(6): 1865-1874, (2013).
15. [15] Ali, S.M., Abood, L.K., Abdoon, R.S., “Brain Tumor Extraction in MRI images using Clustering and Morphological Operations Techniques”, International Journal of Geographical Information System Applications and Remote Sensing, 4(1), (2013).
16. [16] Georgantzoglou, A., Silvia, J., Jena, R., “Image Processing with MATLAB and GPU-Open access peer reviewed chapter”. September, (2014).
17. [17] Saini, L.K., Shrivastava, V., “Analysis of Attacks on Hybrid DWT-DCT Algorithm for Digital Image Watermarking With MATLAB”, Cryptography and Security, 2(3):123-125, (2014).
18. [18] Suzuki, H., Toriwaki, J., “Automatic Segmentation Of Head MRI Images By Knowledge Guided Thresholding”, Computerized Medical Imaging and Graphics, 15(4): 223-240, (1991).
19. [19] Yu-qian, Z., Wei-hua, G., Zhen-cheng, C., Jing-tian, T., Ling-yun, L., “Medical Images Edge Detection Based on Mathematical Morphology”, IEEE Engineering in Medicine and Biology 27th Annual Conference, (2005).
20. [20] Yuskevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., “User-Guided 3D Active Contour Segmentation Of Anatomical Structures: Significantly Improved Efficiency And Reliability”, NeuroImage, 31(3):1116-1128, (2006).
21. [21] Dhawan, A. P., Chitre, Y., Kaiser-Bonasso, C., “Analysis Of Mammographic Microcalcifications Using Gray-Level Image Structure Features”, IEEE Transactions on Medical Imaging, 15(3), (1996).
22. [22] Sujji, G.E., Lakshmi, Y.V.S., Jiji, G.W., “MRI Brain Image Segmentation Based On Thresholding”, International Journal of Advanced Computer Research, 3(1), (2013).
23. [23] Yuncong, F., Haiying, Z., Xiongfei, L., Xiaoli, Z., Hongpeng, L., “A Multi-Scale 3D Otsu Thresholding For Medical Image Segmentation”, Digital Signal Processing, 60, 186-199, (2017).
24. [24] Manikandan, S., Ramar, K., Ihuthayarajan, M.W., Srinivasagan, K.G., “Multilevel Thresholding For Segmentation Of Medical Brain Images Using Real Coded Genetic Algorithm”, Measurement, 47, 558-568, (2014).
25. [25] Ming-Ni, W., Chia-Chen, L., Chin Chen, C., “Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation”, IEEE Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, (2017).
26. [26] Dzialowski, I., Hill, M.D., Coutts, S.B. Demchuk. A.M., Kent, D.M., Wunderlich, O., vom Kummer, R., “Extent of Early Ischemic Changes on Computed Tomography (CT) Before Thrombolysis”, Stroke, 37(4):973-978, (2006).
27. [27] Chen, C.W., Luo, J., Parker, K.J., “Image Segmentation via Adaptive K mean Clustering and Knowledge-Based Morphological Operations with Biomedical Applications”, IEEE Transactions on Image Processing, 7(12):1673- 1683, (1998).
28. [28] Hassoun, M. “Fundamentals of Artificial Neural Networks”, MIT Press, (A Bradford Book), (2003).
29. [29] Gonzales, R.C., Woods, R.E. “Digital Image Processing 4th Edition”, (2017).
30. [30] Mehdy, M.M., Ng, P.Y., Shair, E.F., Md Saleh, N.I., Gomes, C., “Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer” Computational and Mathematical Methods in Medicine, (2017).
31. [31] Shenbagarajan, A., Ramalingam, V., Balasubramanian, C., Palanivel, S., “Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques”, Indian Journal of Science and Technology, 9(1), (2016).
32. [32] Khehra, B.S., Pharwaha, A.P.S., “Classifcation of Clustered Microcalcifications Using A MLFFBP-ANN and SVM”, Egyptian Informatics Journal, 17(1):11-20, (2016).
33. [33] Sankupellay, M., Konovalov, M., “Bird Call Recognition Using Deep Convolutional Neural Network, ResNet-50”, Acoustics, (2018).
34. [34] Macêdo, D., Zanchettin, C., Oliveira, A., Ludermir, T., “Enhancing Batch Normalized Convolutional Networks Using Displaced Rectifier Linear Units: A Systematic Comparative Study”, Expert Systems with Applications, 124, 271-281, (2019).
35. [35] Kawahara, J., Hamarneh, G., “Multi- Resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers, In: Wang L, Adeli E, Wang Q, Shi Y, Suk HI. (eds) Machine Learning in Medical Imaging (MLMI)”, Lecture Notes in Computer Science. 10019, Springer, Cham, (2016).
36. [36] Biswas, M., Kuppili, V., Araki, T., Edla, D.R., Godia, E.C., Suri, H.S., Omerzu, T., Laird, J.R., Khanna, N.N., Nicolaides, A., Suri J.S., “Deep learning strategy for acute carotid intima-media thickness measurement: an ultrasound study on japanese diabetic cohort”, Computers in Biology and Medicine. 98:100-117, (2018).
37. [37] Bacchi, S., Zerner, T., Oakden-Rayner, L, Kleinig, T., Patel, S., Jannes, J., “Deep learning in the prediction of ischemic stroke thrombolysis functional outcomes: A pilot study”, Academic Radiology, (2019).
38. [38] Tyan, Y.S., Ming-Chi, W., Chiun-Li, C., Yu-Liang, K., Ming-Sian, Lee, Hao,Yan, C., “Ischemic stroke detection system with a computer aided diagnostic ability using an unsupervised feature perception enhancement method”, Internatinal Journal of Biomedical Imaging, (2014).
39. [39] Chen-Ying, H., Wei-Chen, C., Po-Tsun, L., Chin-Heng, L., Chi-Chun, L., “Comparing deep neural networks and other machine learning algorithms for stroke prediction in a large scale population-based electronic medical claims database”, In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3110-3113, (2017).
40. [40] Qureshi, A.A., Canxiu, Z., Rong, Z. and Elmeligi, A., “Ischemic stroke detection using EEG signals”, In Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering. 301-308, (2018).