Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks

Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the auto...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2019-09, Vol.23 (5), p.1911-1919
Main Authors: Razzak, Muhammad Imran, Imran, Muhammad, Xu, Guandong
Format: Article
Language:English
Subjects:
2PG-CNN
Algorithms
Architecture
Artificial neural networks
Biomedical imaging
Brain
Brain - diagnostic imaging
Brain architecture
Brain cancer
Brain modeling
Brain Neoplasms - diagnostic imaging
Brain tumor
Brain tumors
cascade CNN
CNN
Computational neuroscience
Databases, Factual
deep neural network
Diagnosis
Embedding
group CNN
group convolutional neural networks
Humans
Image Interpretation, Computer-Assisted - methods
Image processing
Image segmentation
Machine learning
Magnetic Resonance Imaging
Neural networks
Neural Networks, Computer
Task analysis
Tumors
two-pathway CNN
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2018.2874033