A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

Abstract The segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas’ significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved.

[1]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[2]  Tao Xu,et al.  SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation , 2017, Neuroinformatics.

[3]  Nicholas Ayache,et al.  A Generative Model for Brain Tumor Segmentation in Multi-Modal Images , 2010, MICCAI.

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

[5]  Erwin G. Van Meir,et al.  Exciting New Advances in Neuro‐Oncology: The Avenue to a Cure for Malignant Glioma , 2010, CA: a cancer journal for clinicians.

[6]  Islem Rekik,et al.  Bayesian Network and Structured Random Forest Cooperative Deep Learning for Automatic Multi-label Brain Tumor Segmentation , 2018, ICAART.

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

[8]  Jian Yang,et al.  Convolution Neural Networks With Two Pathways for Image Style Recognition , 2017, IEEE Transactions on Image Processing.

[9]  Yong Fan,et al.  A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..

[10]  Shie Mannor,et al.  A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..

[11]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

[12]  Christos Davatzikos,et al.  GLISTR: Glioma Image Segmentation and Registration , 2012, IEEE Transactions on Medical Imaging.

[13]  Mingming Wang,et al.  Multi-path Convolutional Neural Networks for Complex Image Classification , 2015, ArXiv.

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

[15]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[16]  Syed Muhammad Anwar,et al.  Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network , 2017, Neurocomputing.

[17]  Fred A. Hamprecht,et al.  Multi-modal Brain Tumor Segmentation using Deep Convolutional Neural Networks , 2014 .

[18]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[19]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[20]  Stefan Bauer,et al.  Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization , 2011, MICCAI.

[21]  Antonio Criminisi,et al.  Segmentation of Brain Tumor Tissues with Convolutional Neural Networks , 2014 .

[22]  Jianjun Hou,et al.  Learning two-pathway convolutional neural networks for categorizing scene images , 2017, Multimedia Tools and Applications.

[23]  Klaus H. Maier-Hein,et al.  DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images , 2024, IEEE Transactions on Medical Imaging.

[24]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Higino Correia,et al.  Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  Ben Glocker,et al.  Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR , 2012, MICCAI.

[28]  Victor Alves,et al.  Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI , 2018, MICCAI.

[29]  Stephen J. McKenna,et al.  Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation , 2017, MICCAI.

[30]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

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

[32]  Reuven Y. Rubinstein,et al.  Optimization of computer simulation models with rare events , 1997 .

[33]  Brian B. Avants,et al.  Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR , 2014, Neuroinformatics.

[34]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

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