Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales
This work addresses the critical need for early detection of brain tumors, which cause one in four cancer deaths, by improving segmentation accuracy for medical imaging, though it appears incremental as it builds on existing deep learning techniques.
The paper tackled brain tumor segmentation in MRI images by using a cascaded deep neural network with multiple image scales to improve accuracy, achieving higher segmentation accuracy compared to existing methods.
Intracranial tumors are groups of cells that usually grow uncontrollably. One out of four cancer deaths is due to brain tumors. Early detection and evaluation of brain tumors is an essential preventive medical step that is performed by magnetic resonance imaging (MRI). Many segmentation techniques exist for this purpose. Low segmentation accuracy is the main drawback of existing methods. In this paper, we use a deep learning method to boost the accuracy of tumor segmentation in MR images. Cascade approach is used with multiple scales of images to induce both local and global views and help the network to reach higher accuracies. Our experimental results show that using multiple scales and the utilization of two cascade networks is advantageous.