Disease Stage Classification in Glioma Histopathology Images

Abstract:

Computational pathology using computer vision techniques are required to standardize the disease stage evaluation for Glioblastoma multiforme (GBM). In the following works, we evaluate:

We test the performance of various feature descriptors for predicting disease stages of Gliomas and benchmark deep learning models for disease stage classification and subtypes clustering of different GBMs.

Disease Stage Classification and Significant of Features [1]

References:


[1] K. Fukuma, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Feature extraction and disease stage classification for glioma histopathology images. IEEE 17th International Conference on e-Health Networking, Applications and Services (Healthcom), Boston, USA. Proc. IEEE, pp. 598-599, Oct 2015. doi:10.1109/HealthCom.2015.7454574


[2] K. Fukuma, V. B. S. Prasath, H. Kawanaka, B. J. Aronow, H. Takase. A study on feature extraction and disease stage classification for glioma pathology images. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada. Proc. IEEE, pp. 2150-2156, July 2016. doi:10.1109/FUZZ-IEEE.2016.7737958 


[3]  K. Fukuma, V. B. S. Prasath, H. Kawanaka, B. J. Aronow, H. Takase. A study on nuclei segmentation, feature extraction and disease stage classification for human brain histopathological images. International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), York, UK, Sep 2016. Procedia Computer Science, 96, pp. 1202-1020, Sep 2016. doi:10.1016/j.procs.2016.08.164


[4] A. Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Glioblastoma Multiforme tissue histopathology images based disease stage classifica- tion with deep CNN. 6th International Conference on Informatics, Electronics & Vision (ICIEV), Himeji, Hyogo, Japan. Proc. IEEE, September 2017. doi:10.1109/ICIEV.2017.8338558 (Best Paper Award Finalist)


[5] A. Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Improving the generalization of disease stage classification with deep CNN for glioma histopathological images. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics (DLB2H), Kansas, MO, USA. Proc. IEEE, pp. 1222-1226, November 2017. doi:10.1109/BIBM.2017.8217831 


[6] A. Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Arronow, S. Tsuruoka. Glioma subtypes clustering method using histopathological image analysis. 7th International Conference on Informatics, Electronics and Vision (ICIEV), and 2nd International Conference on Imaging, Vision and Pattern Recognition (icIVPR), Fukuoka, Japan. Proc. IEEE, pp. 442-446, June 2018. doi:10.1109/ICIEV.2018.8641031


[7] A. Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network. Biomedical Engineering Letters, 8(3), 321-327, August 2018. doi:10.1007/s13534-018-0077-0


[8] R. Nakagaki,  S. S. Debsarkar, H. Kawanaka, B. Aronow, V. B. S. Prasath. Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data. Submitted, 2023.


In preparation:


Nuclei segmentation and feature extraction for disease stage classification in Glioblastoma Multiforme.

Automatic nuclei segmentation in glioma histopathology with adaptive filtering based entropy thresholding. 

Explainable deep learning models for automatic disease stage prediction in glioma histopathology images.


MS Theses:


Kiichi Fukuma. Feature extraction and disease stage classification for glioma histopathology images. Graduate School of Engineering, Mie University, Japan, 2016.

Asami Yonekura. A study on subtypes clustering of glioma image using cell nuclei features. Graduate School of Engineering, Mie University, Japan, 2016.

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