Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images

• Published in: Computer methods and programs in biomedicine 2018-11, Vol.166, p.39-49
• Main Authors: naceur, Mostefa Ben, Saouli, Rachida, Akil, Mohamed, Kachouri, Rostom
• Format: Article
• Language: English
• Subjects: Algorithms; Brain - diagnostic imaging; Brain - pathology; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumor segmentation; Computer Science; Computer Vision and Pattern Recognition; Convolutional neural networks; Deep learning; Diagnosis, Computer-Assisted - methods; Fully automatic; Glioblastoma - diagnostic imaging; Glioblastoma - pathology; Humans; Hyper-parameters; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging; Medical Imaging; Neural and Evolutionary Computing; Neural Networks (Computer); Pattern Recognition, Automated; Signal and Image Processing; Training
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2018.09.007

•A new fully automatic end-to-end deep learning model for brain tumor segmentation.•EnsembleNet uses Ensemble Learning to aggregate the results of two models which are based on an incremental deep neural network.•We present a new training strategy which unifies the training methods of deep learning models.•The new method of designing deep learning models with the advantage of GPU implementation, our models are one order of magnitude faster compared to the state-of-the-art. [Display omitted] Background and Objective: Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Glioblastomas have four properties: different sizes, shapes, contrasts, in addition, Glioblastomas appear anywhere in the brain. Methods: In this paper, we propose three end-to-end Incremental Deep Convolutional Neural Networks models for fully automatic Brain Tumor Segmentation. Our proposed models are different from the other CNNs-based models that follow the technique of trial and error process which does not use any guided approach to get the suitable hyper-parameters. Moreover, we adopt the technique of Ensemble Learning to design a more efficient model. For solving the problem of training CNNs model, we propose a new training strategy which takes into account the most influencing hyper-parameters by bounding and setting a roof to these hyper-parameters to accelerate the training. Results: Our experiment results reported on BRATS-2017 dataset. The proposed deep learning models achieve the state-of-the-art performance without any post-processing operations. Indeed, our models achieve in average 0.88 Dice score over the complete region. Moreover, the efficient design with the advantage of GPU implementation, allows our three deep learning models to achieve brain segmentation results in average 20.87 s. Conclusions: The proposed deep learning models are effective for the segmentation of brain tumors and allow to obtain high accurate results. Moreover, the proposed models could help the physician experts to reduce the time of diagnostic.