Brain Age Estimation from MRI Images using 2D-CNN instead of 3D-CNN

Öz

Human Brain Age has become a popular aging biomarker and is used to detect differences among healthy individuals. Because of the specific changes in the human brain with aging, it is possible to estimate patients’ brain ages from their brain images. Due to developments of the ability of CNN in classification and regression from images, in this study, one of the most popular state of the art models, the DenseNet model, is utilized to estimate human brain ages using transfer learning. Since this process requires high memory load with 3D-CNN, 2D-CNN is preferred for the task of Brain Age Estimation (BAE). In this study, some experiments are carried out to reduce the number of computations while preserving the total performance. With this aim, center slices of each three brain planes are used as the inputs of the DenseNet model, and different optimizers such as Adam, Adamax and Adagrad are used for each model. The dataset is selected from the IXI (Information Extraction from Images) MRI data repository. The MAE evaluation metric is used for each model with different input set to evaluate performance. The best achieved Mean Absolute Error (MAE) is 6.3 with the input set which consisted of center slices of the sagittal plane of brain scan and the Adamax parameter.

Anahtar Kelimeler

• Brain Age Estimation
• Brain MRI
• 2D-CNN

MR Görüntülerinden 3D-ESA Yerine 2D-ESA Kullanılarak Beyin Yaşı Tahmini

Öz

İnsan Beyin Yaşı, son zamanlarda popüler bir yaşlanma biyobelirteci haline geldi ve sağlıklı kişiler arasındaki farklılıkları tespit etmek için kullanıldı. Yaşlanmayla birlikte insan beynindeki spesifik değişiklikler nedeniyle, hastaların beyin yaşlarını beyin görüntülerinden tahmin etmek mümkündür. Evrişimsel Sinir Ağlarının (ESA) gelişen görüntü sınıflama ve regresyon yeteneğinden yola çıkılarak, bu çalışmada en popüler ESA modellerinden biri olan DenseNet modeli öğrenme aktarımı yöntemiyle kullanılarak insan beyni yaşı tahmini g erçekleştirilmiştir. 3D-ESA y üksek bellek yükü gerektirdiğinden Beyin Yaşı Tahmin (BAE) görevi için 2D-CNN tercih edilmiştir. Bu deneyde, toplam performans korunurken hesaplama yükünü azaltmak için bazı deneyler yapılmıştır. Bu amaçla, her üç beyin düzleminin merkez dilimleri DenseNet modelinin girdileri olarak kullanılmıştır ve her model için Adam, Adamax ve Adagrad gibi f arklı optimizerlar k ullanılmıştır. Veri k ümesi, I XI M RI veri havuzundan seçilmiştir. Performansı değerlendirmek için ortalama mutlak hata (MAE) metriği her model için kullanılmıştır. Bu çalışmada en düşük Ortalama Mutlak Hata (MAE), beynin sagital düzleminin merkez dilimlerini içeren giriş kümesiyle ve Adamax parametresiyle 6.3 olarak elde edilmiştir.

Anahtar Kelimeler

• Beyin Yaşı Tahmini
• Beyin MR
• 2D-ESA

Kaynakça

1. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 1800–1807. https://doi.org/10.1109/CVPR.2017.195
2. Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017). Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage, 163, 115–124. https://doi.org/10.1016/j.neuroimage.2017.07.059
3. Darici, M. B., Dokur, Z., & Olmez, T. (2020). Pneumonia Detection and Classification Using Deep Learning on Chest X-Ray Images. International Journal of Intelligent Systems and Applications in Engineering, 8(4), 177–183.
4. Darıcı, M. B. (2020). Göğüs kafesi röntgen görüntülerinde derin öğrenme metoduyla zatürre hastalığının tanısı (Vol. 21, Issue 1). https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp
5. Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
6. Franke, K., & Gaser, C. (2012). Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s Disease. GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry, 25(4), 235–245. https://doi.org/10.1024/1662-9647/a000074
7. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., & Zijdenbos, A. (1999). . Neuropsychol, Dev.
8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770–778. https://doi.org/10.1109/CVPR.2016.90

9. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. ArXiv.
10. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 2261–2269. https://doi.org/10.1109/CVPR.2017.243
11. Huang, T., Chen, H., Fujimoto, R., Ito, K., Wu, K., Sato, K., Taki, Y., Fukuda, H., & Aoki, T. (2017). AGE ESTIMATION FROM BRAIN MRI IMAGES USING DEEP LEARNING Department of Computer Science , National Tsing-Hua University , Taiwan Graduate School of Information Science , Tohoku University , Japan South China University of Technology , China Institute of D. Conference: 2017 IEEE 14th International Symposium on Biomedical, 2(1), 849–852.
12. Ito, K., Fujimoto, R., Huang, T. W., Chen, H. T., Wu, K., Sato, K., Taki, Y., Fukuda, H., & Aoki, T. (2018). Performance Evaluation of Age Estimation from T1-Weighted Images Using Brain Local Features and CNN. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July, 694–697. https://doi.org/10.1109/EMBC.2018.8512443
13. Jia Deng, Wei Dong, Socher, R., Li-Jia Li, Kai Li, & Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 248–255. https://doi.org/10.1109/cvprw.2009.5206848
14. Jiang, H., Lu, N., Chen, K., Yao, L., Li, K., Zhang, J., & Guo, X. (2020). Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks. Frontiers in Neurology, 10(January). https://doi.org/10.3389/fneur.2019.01346
15. Krizhevsky, A. (2009). Learning Multiple Layers of Features from Tiny Images. Asha, 34(4).
16. Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30(6), 718–729. https://doi.org/10.1016/j.neubiorev.2006.06.001
17. Levakov, G., Rosenthal, G., Shelef, I., Raviv, T. R., & Avidan, G. (2020). From a deep learning model back to the brain—Identifying regional predictors and their relation to aging. Human Brain Mapping, 41(12), 3235–3252. https://doi.org/https://doi.org/10.18201/ijisae.2020466310
18. Li, H., Satterthwaite, T. D., & Fan, Y. (2018). Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks. ArXiv, Isbi, 101–104.
19. Liem, F., Varoquaux, G., Kynast, J., Beyer, F., Kharabian Masouleh, S., Huntenburg, J. M., Lampe, L., Rahim, M., Abraham, A., Craddock, R. C., Riedel-Heller, S., Luck, T., Loeffler, M., Schroeter, M. L., Witte, A. V., Villringer, A., & Margulies, D. S. (2017). Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage, 148(November 2016), 179–188. https://doi.org/10.1016/j.neuroimage.2016.11.005
20. Luders, E., Gaser, C., Narr, K. L., & Toga, A. W. (2009). Why sex matters: Brain size independent differences in gray matter distributions between men and women. Journal of Neuroscience, 29(45), 14265–14270. https://doi.org/10.1523/JNEUROSCI.2261-09.2009
21. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., & Ng, A. Y. (1952). PROFESSOR V.N. SHamov. Voprosy Neǐrokhirurgii, 16(5), 9–13. Pardakhti, N., & Sajedi, H. (2020). Brain age estimation based on 3D MRI images using 3D convolutional neural network. Multimedia Tools and Applications, 79(33–34), 25051–25065. https://doi.org/10.1007/s11042-020-09121-z
22. Rokicki, J., Wolfers, T., Nordhøy, W., Tesli, N., Quintana, D. S., Alnæs, D., Richard, G., de Lange, A. M. G., Lund, M. J., Norbom, L., Agartz, I., Melle, I., Nærland, T., Selbæk, G., Persson, K., Nordvik, J. E., Schwarz, E., Andreassen, O. A., Kaufmann, T., & Westlye, L. T. (2020). Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Human Brain Mapping, August 2020, 1714–1726. https://doi.org/10.1002/hbm.25323
23. Rossi, A., Vannuccini, G., Andreini, P., Bonechi, S., Giacomini, G., Scarselli, F., & Bianchini, M. (2019). Analysis of brain NMR images for age estimation with deep learning. Procedia Computer Science, 159, 981–989. https://doi.org/10.1016/j.procs.2019.09.265
24. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14.
25. Symposium, I., & Imaging, B. (2019). AN AGE ESTIMATION METHOD USING 3D-CNN FROM BRAIN MRI IMAGES Graduate School of Information Sciences , Tohoku University , Japan . South China University of Technology , China . Institute of Divelopment, Aging and Cancer, Tohoku University, Japan . 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Isbi, 380–383.
26. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 1–9. https://doi.org/10.1109/CVPR.2015.7298594
27. Tang, J., Rangayyan, R. M., Xu, J., El Naqa, I. E., & Yang, Y. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Transactions on Information Technology in Biomedicine, 13(2), 236–251. https://doi.org/10.1109/TITB.2008.2009441
28. Wang, J., Li, W., Miao, W., Dai, D., Hua, J., & He, H. (2014). Age estimation using cortical surface pattern combining thickness with curvatures. Medical and Biological Engineering and Computing, 52(4), 331–341. https://doi.org/10.1007/s11517-013-1131-9
29. Yin, T. K., & Chiu, N. T. (2004). A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach. IEEE Transactions on Medical Imaging, 23(5), 639–654. https://doi.org/10.1109/TMI.2004.826355