Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks

2024/02/12 by Kimberly Helm, Helm, Kimberly, Tejas Sudharshan Mathai +9 · 2 citations
Engineering · Medicine · Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2402.08098

openalex publication_date 2024/02/12 · openalex created_date 2024/02/15 · openalex updated_date 2026/07/28

Abstract

score of 99.5% at differentiating 5 common MRI sequences obtained by three Siemens scanners (Aera, Verio, Biograph mMR). To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis, and our work has outperformed the previous state-of-the-art MRI series classifiers.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Cited by

• Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks
• Classification of Multi-Parametric Body MRI Series Using Deep Learning

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