QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors
This work provides an efficient and reliable automated segmentation tool for clinicians, aiming to improve early detection and quantification of brain tumors.
This paper addresses the time-consuming and subjective nature of manual brain tumor segmentation from MRI scans. The authors developed QuickTumorNet, an automated method that achieved fast, reliable, and accurate multi-class segmentation of meningioma, glioma, and pituitary tumors.
Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and reliable method for brain tumor detection and quantification. In this study, we propose an end-to-end approach for brain tumor segmentation, capitalizing on a modified version of QuickNAT, a brain tissue type segmentation deep convolutional neural network (CNN). Our method was evaluated on a data set of 233 patient's T1 weighted images containing three tumor type classes annotated (meningioma, glioma, and pituitary). Our model, QuickTumorNet, demonstrated fast, reliable, and accurate brain tumor segmentation that can be utilized to assist clinicians in diagnosis and treatment.