Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)

Research output: Contribution to journalConference articlepeer-review

Abstract

Magnetic Resonance Imaging (MRI) is a widely-accepted imaging technique for knee injury analysis. Its advantage of capturing knee structure in three dimensions makes it the ideal tool for radiologists to locate potential tears in the knee. In order to better confront the ever growing workload of musculoskeletal (MSK) radiologists, automated tools for patients’ triage are becoming a real need, reducing delays in the reading of pathological cases. In this work, we present the Efficiently-Layered Network (ELNet), a convolutional neural network (CNN) architecture optimized for the task of initial knee MRI diagnosis for triage. Unlike past approaches, we train ELNet from scratch instead of using a transfer-learning approach. The proposed method is validated quantitatively and qualitatively, and compares favorably against state-of-the-art MRNet while using a single imaging stack (axial or coronal) as input. Additionally, we demonstrate our model’s capability to locate tears in the knee despite the absence of localization information during training. Lastly, the proposed model is extremely lightweight (< 1MB) and therefore easy to train and deploy in real clinical settings.

Original languageEnglish
Pages (from-to)784-794
Number of pages11
JournalProceedings of Machine Learning Research
Volume121
StatePublished - 2020
Event3rd Conference on Medical Imaging with Deep Learning, MIDL 2020 - Virtual, Online, Canada
Duration: 6 Jul 20208 Jul 2020

Keywords

  • ACL Tear
  • Deep Learning
  • Knee Diagnosis
  • Knee Injury
  • MRI
  • Medical Triage
  • Meniscus Tear

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