Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice.So, automatic and reliable segmentation methods are required;however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem.
Accurate tumor segmentation is an essential and crucial step for computer-aided brain tumor diagnosis and surgical planning. Subjective segmentations are widely adopted in clinical diagnosis and treating, but they are neither accurate nor reliable. An automatical and objective system for brain tumor segmentation is strongly expected. But they are still facing some challenges such as lower segmentation accuracy, demanding a priori knowledge or requiring the human intervention. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. The final performance shows that the proposed brain tumor segmentation method is more accurate and efficient.
Segmentation of brain image is imperative in surgical planning and treatment planning in the field of medicine. In this work, we have proposed a computer aided system for brain MR image segmentation for detection of tumor location using K - means clustering algorithm followed by morphological filtering. We were able to segment tumor from different brain MRI images from our database.
This provide a novel deep learning based algorithm for segmenting the brain tumor. However, the deep learning algorithm not only can automatically segment the brain tumor, but also can learn a deep nonlinear network, realizes the approximation of complex function,and describes the input data distribution. In this paper, we integrate the Stacked Denoising Auto Encoder into the segmentation procedure, and combine it with the preprocessing and post-processing steps so as to improve the segmentation result. The proposed method has the ability to achieve higher classification accuracy and obtain a good matching rate between the segmentation result and the ground truth
Keywords — Diagnosis, subjective segmentation, autoencoder ,tumor segmentation, Brain tumor segmentation; Brain tumor detection,Computer Aided Diagnosis (CAD); Deep Learning; Stacked Denoising Auto-Encoder (SDAE) ;Stacked Auto-Encoder (SAE).