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Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials

  • Andrew Doyle
  • Colm Elliott
  • Zahra Karimaghaloo
  • Nagesh Subbanna
  • Douglas L. Arnold
  • Tal Arbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

A variety of automatic segmentation techniques have been successfully applied to the delineation of larger T2 lesions in patient MRI in the context of Multiple Sclerosis (MS), assisting in the estimation of lesion volume, a common clinical measure of disease activity and stage. In the context of clinical trials, however, a wider number of metrics are required to determine the “burden of disease” and activity in order to measure treatment efficacy. These include: (1) the number and volume of T2 lesions in MRI, (2) the number of new and enlarging T2 volumes in longitudinal MRI, and (3) the number of gadolinium enhancing lesions in T1 MRI, the portion of lesions that enhance in T1w MRI after injection with a contrast agent, often associated with active inflammations. In this context, accurate lesion detection must ensure that even small lesions (e.g. 3 to 10 voxels) are detected as they are prevalent in trials. Manual or semi-manual approaches are too time-consuming, inconsistent and expensive to be practical in large clinical trials. To this end, we present a series of fully-automatic, probabilistic machine learning frameworks to detect and segment all lesions in patient MRI, and show their accuracy and robustness in large multi-center, multi-scanner, clinical trial datasets. Several of these algorithms have been placed into a commercial software analysis pipeline, where they have assisted in improving the efficiency and precision of the development of most new MS treatments worldwide. Recent work has shown how a new Bag-of-Lesions brain representation can be used in the context of clinical trials to automatically predict the probability of future disease activity and potential treatment responders, leading to the possibility of personalized medicine.

Notes

Acknowledgements

This work was supported by a Canadian Natural Science and Engineering Research Council collaborative Research and Development Grant (CRDPJ 411455-10), and an International Progressive MS Alliance Collaborative Network Award (PA-1603-08175).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Andrew Doyle
    • 1
  • Colm Elliott
    • 1
  • Zahra Karimaghaloo
    • 1
  • Nagesh Subbanna
    • 1
  • Douglas L. Arnold
    • 2
  • Tal Arbel
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontréalCanada
  2. 2.NeuroRx ResearchMontréalCanada

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