Abstract
Reliable localization of abnormal lymph nodes in T2 Magnetic Resonance Imaging (MRI) scans is needed for staging and treatment of lymphoproliferative diseases. Radiologists need to accurately characterize the size and shape of the lymph nodes and may require an additional contrast sequence such as diffusion weighted imaging (DWI) for staging confirmation. The varied appearance of lymph nodes in T2 MRI makes staging for metastasis challenging. Moreover, radiologists often times miss smaller lymph nodes that could be malignant over the course of a busy clinical day. To address these imaging and workflow issues, in this pilot work we aim to localize potentially suspicious lymph nodes for staging. We use state-of-the-art detection neural networks to localize lymph nodes in T2 MRI scans acquired through a variety of scanners and exam protocols, and employ bounding box fusion techniques to reduce false positives (FP) and boost detection accuracy. We construct an ensemble of the best detection models to identify potential lymph node candidates for staging, obtaining a 71.75% precision and 91.96% sensitivity at 4 FP per image. To the best of our knowledge, our results improve upon the current state-of-the-art techniques for lymph node detection in T2 MRI scans.
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Acknowledgements
This work was supported by the Intramural Research Programs of the NIH National Library of Medicine and NIH Clinical Center. We also thank Jaclyn Burge for the helpful comments and suggestions.
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Mathai, T.S. et al. (2021). Detection of Lymph Nodes in T2 MRI Using Neural Network Ensembles. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_70
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DOI: https://doi.org/10.1007/978-3-030-87589-3_70
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