Semantic Segmentation of Brain Tumor from 3D Structural MRI Using U-Net Autoencoder

Abstract

Automated semantic segmentation of brain tumors from 3D MRI images plays a significant role in medical image processing, monitoring and diagnosis. Early detection of these brain tumors is highly requisite for the treatment, diagnosis and surgical pre-planning of the anomalies. The physicians normally follow the manual way of delineation for diagnosis of tumors which is time consuming and requires too much knowledge of anatomy. To resolve these limitations, convolutional neural network (CNN) based U-Net autoencoder model is proposed which performs automated segmentation of brain tumors from 3D MRI brain images by extracting the key features of the tumor. Additionally, Image normalization, image augmentation, image binarization etc. are applied for data pre-processing. Later on, the model is applied to the new 3D MRI brain images to test the accuracy of it. Applying the proposed method, the accuracy is obtained upto 96.06% considering the 18 subjects. Finally, this approach is a well-structured model for segmenting the tumor region from MRI brain images as compare to the other existing models which may assist the physicians for better diagnosis and therefore, opening the door for more precise therapy and better treatment to the patient.