Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network
This work addresses the time-consuming and variable manual segmentation problem for radiologists, though it appears incremental as it focuses on optimizing existing deep learning methods for specific MR data.
The study tackled automated brain tumor segmentation from multiple MR sequences using a deep convolutional neural network, achieving high accuracy by identifying the most effective sequence combinations.
Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation. The objective of this study is to automate the delineation of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted contrast-enhanced MR sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.