Deep Learning-based Type Identification of Volumetric MRI Sequences
This addresses a domain-specific issue for clinical professionals and researchers in medical imaging by automating sequence identification, but it is incremental as it applies an existing method to a new task.
The paper tackles the problem of identifying brain MRI sequence types (FLAIR, T1, T1c, T2) due to unstandardized naming, which hinders automated analysis and dataset generation, by proposing a deep learning system based on a ResNet CNN that achieves 96.81% accuracy on public datasets.
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume MRI analysis increases, it becomes convenient to have each sequence well identified. However, the unstandardized naming of MRI sequences makes their identification difficult for automated systems, as well as makes it difficult for researches to generate or use datasets for machine learning research. In the face of that, we propose a system for identifying types of brain MRI sequences based on deep learning. By training a Convolutional Neural Network (CNN) based on 18-layer ResNet architecture, our system can classify a volumetric brain MRI as a FLAIR, T1, T1c or T2 sequence, or whether it does not belong to any of these classes. The network was evaluated on publicly available datasets comprising both, pre-processed (BraTS dataset) and non-pre-processed (TCGA-GBM dataset), image types with diverse acquisition protocols, requiring only a few slices of the volume for training. Our system can classify among sequence types with an accuracy of 96.81%.