Derin Öğrenme İle Beyin Tümör Segmentasyonu

Year 2024, , 159 - 174, 31.07.2024

Bilal Taşdemir , Necaattin Barışçı

https://doi.org/10.17671/gazibtd.1396872

Abstract

Artan nüfus ile birlikte her geçen gün daha fazla insan beyin tümöründen etkilenmektedir. Diğer hastalıklar ile kıyaslandığında beyin tümörünün ölüm oranı çok daha yüksektir. Ayrıca beyin tümörü hastalığına yakalanan bireyler günlük yaşamlarında önemli zorluklarla karşılaşmaktadır. Beyin tümörünün tanısı doktorlar için zorlu bir süreçtir. Teşhis aşamasında doktorların karşılaştığı zorluklar ve bu sorunların üstesinden gelmek adına bir çözüm önerisi olarak, beyin MR görüntülerinin otomatik segmentasyonunu sağlayacak bir modelin tasarlanması hedeflenmiştir. Bu çalışmada beyin MR görüntülerinin segmentasyonundaki zorluğu aşmak için topluluk öğrenimi yöntemi kullanılmıştır. Topluluk öğrenimi yönteminde derin öğrenme tabanlı dikkat mekanizmalı u-net ve u-net modelleri kullanılmıştır. Bu yöntem ile iki farklı modelden gelen tahmin değerlerinin ortalamasının alınması ve daha kararlı bir modelin geliştirilmesi amaçlanmıştır. Model eğitimi için BRATS veri setinin 2018, 2019 ve 2020 versiyonları kullanılırken, model testleri için 2017 versiyonu tercih edilmiştir. Veri setindeki dengesiz sınıf dağılımı problemine çözüm olarak farklı veri ön işleme adımları kullanılmıştır ve topluluk öğrenimi modeli ile beyin MR görüntülerinin segmentasyon problemi ele alınmıştır. Elde edilen topluluk öğrenimi yöntemi ile BRATS2017 veri seti üzerinde %87,33 ortalama zar skoru, %81,74 nekrotik sınıfı zar skoru, %91,57 ödem sınıfı zar skoru, %76,03 artırılmış tümör sınıfı zar skoru, %99,96 arka plan sınıfı zar skoru ve Tüm Tümör (TT), Çekirdek Tümör (ÇT) ve Artırılmış Tümör (AT) için sırasıyla %83,11, %78,88 ve %76,03 zar skoru elde edilmiştir.

Keywords

Segmentasyon, Tümör, Derin Öğrenme, U-net, Görüntü İşleme, Normalizasyon, Topluluk Öğrenimi

Brain Tumor Segmantation With Deep Learning

Abstract

With the increasing population, more and more people are affected by brain tumors every day. Compared to other diseases, the death rate of brain tumors is much higher. In addition, people suffering from brain tumor disease have important difficulties in their daily lives. The diagnosis of brain tumors poses a challenging process for medical professionals. To address the difficulties faced by doctors during the diagnostic phase and propose a solution, the objective is to design a model that enables the automatic segmentation of brain MR images. In this study, Ensemble learning method was used to overcome the difficulty in segmentation of brain MRI images. Deep learning base attention u-net and u-net models were used in ensemble learning method. The aim is to develop a more stable model by averaging the prediction values from two different models. While the 2018, 2019 and 2020 versions of BRATS dataset were used for model training, the 2017 version was preferred for model testing. With different data preprocessing steps and ensemble learning model, the difficulty of segmentation of brain MR images has been overcome on imbalanced dataset. With the ensemble learning method created, 87.33% average zar score, 81.74% necrotic class zar score, 91.57% edema zar score, 76.03% enhancing zar score, 99.96% background class zar score and zar score of 83.11%, 78.88% and 76.03% for Whole Tumor (WT), Tumor Core (TC) and Enhancing Tumor (ET) were obtained on BRATS2017 dataset.

Keywords

segmantation, tumor, deep learning, u-net, image processing, normalization

References

• Diao, Y., Li, F., Li, Z. (2023). Joint learning-based feature reconstruction and enhanced network for incomplete multi-modal brain tumor segmentation. Computers in Biology and Medicine, 163, 107234.
• Jiang, M., Zhai, F., Kong, J. (2021). A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images, Artificial Intelligence in Medicine, 121, 102180.
• Zhou, T. (2023), Feature fusion and latent feature learning guided brain tumor segmentation and missing modality recovery network. Pattern Recognition, 141, 109665.
• Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y. (2018). A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 43, 98-111.
• Zhang, G., Zhou, J., He, G. and Zhu, H. (2023). Deep fusion of multi-modal features for brain tumor image segmentation. Heliyon, 9 (8).
• Huang, J., Shlobin, N. A., Lam, S. K. and DeCuypere, M. (2022). Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review. World Neurosurgery, 157, 99-105.
• Wang, P. and Chung, A. C. S. (2022). Relax and focus on brain tumor segmentation, Medical Image Analysis, 75 (102259).
• Goceri, E. (2020). CapsNet topology to classify tumours from brain images and comparative evaluation. IET Image Processing, 14, 882-889.
• Alzahrani, S. M. (2023). ConvAttenMixer: Brain Tumor Detection and Type Classification using Convolutional Mixer with External and Self-Attention Mechanisms. Journal of King Saud University - Computer and Information Sciences, 35 (10).
• Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J. and Yu, Y. (2021). Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognition, 110 (107562).
• Naser, M. A. and Deen, M. J. (2020). Brain tumor segmentation and grading of lowergrade glioma using deep learning in MRI images. Computers in Biology and Medicine, 121 (103758).
• Rammurthy, D. and Mahesh, P. K. (2022). Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. Journal of King Saud University - Computer and Information Sciences, 34, 3259-3272.
• Hashemzehi, R., Mahdavi, S. J. S., Kheirabadi, M. and Kamel, S. R. (2020). Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Biocybernetics and Biomedical Engineering, 40, 1225-1232. 58
• Nalepa, J., Lorenzo, P. R., Marcinkiewicz, M., Billewicz, B. B., Wawrzyniak, P., Walczak, M., Kawulok, M., Dudzik, W., Kotowski, K., Burda, I., Machura, B., Mrukwa, G., Ulrych, P. and Hayball, M. P. (2020). Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artificial Intelligence in Medicine, 102 (101769). Naceur, M. B., Akil, M., Saouli, R. and Kachouri, R. (2020). Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Medical Image Analysis, 63 (101692). Deepak, S. and Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111 (103345).
• Maharjan, S., Alsadoon, A., Prasad, P. W. C., Al-Dalain, T. and Alsadoon, O. H. (2020). A novel enhanced softmax loss function for brain tumour detection using deep learning. Journal of Neuroscience Methods, 330 (108520).
• Saba, T., Mohamed, A. S., El-Affendi, M., Amin, J. and Sharif, M. (2020). Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research, 59, 221-230. Mittal, M., Goyal, L. M., Kaur, S., Kaur, I., Verma, A. and Hemanth, D. J. (2019). Deep learning based enhanced tumor segmentation approach for MR brain images. Applied Soft Computing, 78, 346-354.
• Yang, T., Song, J. and Li, L. (2019). A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI. Biocybernetics and Biomedical Engineering, 39, 613-623. Mehrotra, R., Ansari, M. A., Agrawal, R. and Anand, R. S. (2020). A Transfer Learning approach for AI-based classification of brain tumors. Machine Learning with Applications, 2 (100003).
• Xu, W., Yang, H., Zhang, M., Cao, Z., Pan, X. and Liu, W. (2022). Brain tumor segmentation with corner attention and high-dimensional perceptual loss. Biomedical Signal Processing and Control, 73 (103438).
• Liu, Y., Du, J., Vong, C., Yue, G., Yu, J., Wang, Y., Lei, B. and Wang, T. (2022). Scaleadaptive super-feature based MetricUNet for brain tumor segmentation. Biomedical Signal Processing and Control, 73 (103442).
• Drai, M., Testud, B., Brun, G., Hak, J. F., Scavarda, D., Girard, N. and Stellman, J. P. (2022). Borrowing strength from adults: Transferability of AI algorithms for paediatric brain and tumour segmentation. European Journal of Radiology, 151 (110291).
• Bidkar, P. S., Kumar, R. and Ghosh, A. (2022). SegNet and Salp Water Optimizationdriven Deep Belief Network for Segmentation and Classification of Brain Tumor. Gene Expression Patterns, 45 (119248). Zhou, T., Canu, S., Vera, P. and Ruan, S. (2021). Feature-enhanced generation and multimodality fusion based deep neural network for brain tumor segmentation with missing MR modalities. Neurocomputing, 466, 102-112. 59
• Huang, Z., Zhao, Y., Liu, Y. and Song, G. (2021). GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation. Biomedical Signal Processing and Control, 70 (102958).
• Cinar, N., Ozcan, A. and Kaya, M. (2022). A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images. Biomedical Signal Processing and Control, 76 ( 103647).
• Mazumdar, I. and Mukherjee, J. (2022). Fully automatic MRI brain tumor segmentation using efficient spatial attention convolutional networks with composite loss. Neurocomputing, 500, 243-254.
• Zhou, T., Zhu, S. (2023). Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation. Computers in Biology and Medicine, 163, 107142.
• Hu, J., Gu, X., Wang, Z., Gu, X. (2023). Active consistency network for multi-source domain generalization in brain tumor segmentation. Biomedical Signal Processing and Control, 86, 105132.
• Akbar, A. S., Fatichah, C., Suciati, N. (2022). Single level UNet3D with multipath residual attention block for brain tumor segmentation. Journal of King Saud University - Computer and Information Sciences, 34, 3247-3258.
• Taşdemir, B., Barışçı, N. (2023). Dynamic Image Scaling for Imbalanced Brain MRI Dataset. International Conference on Global Practice of Multidisciplinary Scientific Studies-IV Turkish Republic of Northern Cyprus, 2710-2719.
• Li, H., Li, A., Wang, M. (2019). A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Computers in Biology and Medicine, 108, 150-160.
• Wang, G., Li, W., Ourselin, S. (2018). Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks. International MICCAI Brainlesion Workshop, 178-190.
• Beers, A., Chang, K., Brown, J. (2017). Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation. arXiv:1709.02967.
• Hu, J., Gu, X., Wang, Z. and Gu, X. (2023). Active consistency network for multi-source domain generalization in brain tumor segmentation. Biomedical Signal Processing and Control, 86 (105132).
• Akbar, A. S., Fatichah, C. and Suciati, N. (2022). Single level UNet3D with multipath residual attention block for brain tumor segmentation. Journal of King Saud University - Computer and Information Sciences, 34, 3247-3258.
• Taşdemir, B. and Barışçı, N. (2023). Dynamic Image Scaling for Imbalanced Brain MRI Dataset. International Conference on Global Practice of Multidisciplinary Scientific Studies-IV Turkish Republic of Northern Cyprus, 2710-2719.