Deep Learning-based Type Identification of Volumetric MRI Sequences

TL;DR

This paper presents a deep learning system using CNNs to automatically identify brain MRI sequence types, improving dataset standardization and aiding clinical and research workflows.

Contribution

The study introduces a CNN-based method that accurately classifies MRI sequences with minimal slices, addressing unstandardized naming issues in MRI datasets.

Findings

Achieved 96.81% classification accuracy.

Effective on both pre-processed and non-pre-processed datasets.

Requires only a few slices for training.

Abstract

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%.