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 seconds.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.

In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. The proposed CNNs model is inspired by the Occipito-Temporal pathway which has a special function called selective attention that uses different receptive field sizes in successive layers to figure out the crucial objects in a scene. Thus, using selective attention technique to develop the CNNs model, helps to maximize the extraction of relevant features from MRI images. We have also treated two more issues: class-imbalance, and the spatial relationship among image Patches. To address the first issue, we propose two steps: an equal sampling of images Patches and an experimental analysis of the effect of weighted cross-entropy loss function on the segmentation results. In addition, to overcome the second issue, we have studied the effect of Overlapping Patches against Adjacent Patches where the Overlapping Patches show a better segmentation result due to the introduction of the global context as well as the local features of the image Patches compared to the conventionnel Adjacent Patches method. Our experiment results are reported on BRATS-2018 dataset where our Endto-End Deep Learning model achieved state-of-the-art performance. The median Dice score of our fully automatic segmentation model is 0.90, 0.83, 0.83 for the whole tumor, tumor core, and enhancing tumor respectively compared to the Dice score of radiologist, that is in the range 74%-85%. Moreover, our proposed CNNs model is not only computationally efficient at inference time, but it could segment the whole brain on average 16 seconds. Finally, the proposed Deep Learning model provides an accurate and reliable segmentation result, and that makes it suitable for adopting in research and as a part of different clinical settings.

Prediction methods of glioblastoma tumours growth constitute a hard task due to the lack of medical data, which is mostly related to the patients' privacy, the cost of collecting a large medical data set, and the availability of related notations by experts.In this study, the authors propose a synthetic medical image generator (SMIG) with the purpose of generating synthetic data based on the generative adversarial network in order to provide anonymised data. In addition, to predict the glioblastoma multiform tumour growth the authors developed a tumour growth predictor based on end to end convolution neural network architecture that allows training on a public data set from the cancer imaging archive (TCIA), combined with the generated synthetic data. The authors also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA data set, the obtained results demonstrate valuable tumour growth prediction accuracy.

No abstract

Brain tumor segmentation through MRI images analysis is one of the most challenging issues in medical field. Among these issues, Glioblastomas (GBM) invade the surrounding tissue rather than displacing it, causing unclear boundaries, furthermore, GBM in MRI scans have the same appearance as Gliosis, stroke, inflammation and blood spots. Also, fully automatic brain tumor segmentation methods face other issues such as false positive and false negative regions. In this paper, we present new pipelines to boost the prediction of GBM tumoral regions. These pipelines are based on 3 stages, first stage, we developed Deep Convolutional Neural Networks (DCNNs), then in second stage we extract multi-dimentional features from higher-resolution representation of DCNNs, in third stage we developed machine learning algorithms, where we feed the extracted features from DCNNs into different algorithms such as Random forest (RF) and Logistic regression (LR), and principal component analysis with support vector machine (PCA-SVM). Our experiment results are reported on BRATS-2019 dataset where we achieved through our proposed pipelines the state-of-the-art performance. The average Dice score of our best proposed brain tumor segmentation pipeline is 0.85, 0.76, 0.74 for whole tumor, tumor core, and enhancing tumor, respectively. Finally, our proposed pipeline provides an accurate segmentation performance in addition to the computational efficiency in terms of inference time makes it practical for day-today use in clinical centers and for research.