Brain Tumor Segmentation with Special Emphasis on the Non-Enhancing Brain Tumor Compartment
This work addresses a gap in automated brain tumor segmentation for medical applications, focusing on a compartment often overlooked in recent challenges but important for patient prognosis.
The paper tackles the problem of segmenting non-enhancing brain tumor compartments from MRI data, which are crucial for predicting patient survival and tumor growth, and reports results from a U-Net-based model designed for this task.
A U-Net based deep learning architecture is designed to segment brain tumors as they appear on various MRI modalities. Special emphasis is lent to the non-enhancing tumor compartment. The latter has not been considered anymore in recent brain tumor segmentation challenges like the MICCAI challenges. However, it is considered to be indicative of the survival time of the patient as well as of areas of further tumor growth. Hence it deems essential to have means to automatically delineate its extension within the tumor.