Segmentation of Multi-Modal MRI Brain Tumor Sub-Regions Using Deep Learning

In medical imaging, extraction of brain tumor region in the magnetic resonance image (MRI) is not sufficient, but finding the tumor extension is necessary to plan best treatment to improve the survival rate as it depends on tumor’s size, location, and patient’s age. Manually extracting the brain tumor sub-regions from MRI volume is tedious, time consuming and the inherently complex brain tumor images requires a proficient radiologist. Thus, a reliable multi-modal deep learning models are proposed for automatic segmentation to extract the sub-regions like enhancing tumor (ET), tumor core (TC), and whole tumor (WT). These models are constructed on the basis of U-net and VGG16 architectures. The whole tumor is obtained by segmenting T2-weighted images and cross-check the edema’s extension in T2 fluid attenuated inversion recovery (FLAIR). ET and TC are both extracted by evaluating the hyper-intensities in T1-weighted contrast enhanced images. The proposed method has produced better results in terms of dice similarity index, Jaccard similarity index, accuracy, specificity, and sensitivity for segmented sub regions. The experimental results on BraTS 2018 database shows the proposed DL model outperforms with average dice coefficients of 0.91521, 0.92811, 0.96702, and Jaccard coefficients of 0.84715, 0.88357, 0.93741 for ET, TC, and WT respectively.

[1]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[2]  Sébastien Ourselin,et al.  Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks , 2017, BrainLes@MICCAI.

[3]  Shuo Li,et al.  Glioma Segmentation With a Unified Algorithm in Multimodal MRI Images , 2018, IEEE Access.

[4]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[5]  Jianwei Yuan,et al.  Comparative proteomic study reveals the enhanced immune response with the blockade of interleukin 10 with anti-IL-10 and anti-IL-10 receptor antibodies in human U937 cells , 2019, PloS one.

[6]  Nicholas J. Tustison,et al.  Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features , 2018, Frontiers in Computational Neuroscience.

[7]  Guang Yang,et al.  Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks , 2017, MIUA.

[8]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[9]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

[10]  Tanzila Saba,et al.  Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN) , 2018, Microscopy research and technique.

[11]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[12]  Kai Hu,et al.  Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field , 2019, IEEE Access.

[13]  Klaus H. Maier-Hein,et al.  No New-Net , 2018, 1809.10483.

[14]  Cem Direkoglu,et al.  Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods , 2016 .

[15]  Claudio Pollo,et al.  Atlas-based segmentation of pathological MR brain images using a model of lesion growth , 2004, IEEE Transactions on Medical Imaging.

[16]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[17]  Andriy Myronenko,et al.  3D MRI brain tumor segmentation using autoencoder regularization , 2018, BrainLes@MICCAI.

[18]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[19]  et al.,et al.  Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.

[20]  Jie Yang,et al.  Semi-automated brain tumor and edema segmentation using MRI. , 2005, European journal of radiology.

[21]  Jennie W. Taylor,et al.  The lomustine crisis: awareness and impact of the 1500% price hike. , 2018, Neuro-oncology.

[22]  Jean Stawiaski,et al.  A Pretrained DenseNet Encoder for Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[23]  Raghav Mehta,et al.  3D U-Net for Brain Tumour Segmentation , 2018, BrainLes@MICCAI.

[24]  Richard McKinley,et al.  Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[25]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[26]  Lei Li,et al.  A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI , 2019, Biocybernetics and Biomedical Engineering.

[27]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[28]  Yong Wang,et al.  Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation , 2019, PloS one.