{
  "id": "2002.00952",
  "version": "v1",
  "published": "2020-02-03T16:56:05.000Z",
  "updated": "2020-02-03T16:56:05.000Z",
  "title": "Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data",
  "authors": [
    "Mattias Billast",
    "Maria Ines Meyer",
    "Diana M. Sima",
    "David Robben"
  ],
  "comment": "MICCAI BrainLes 2019 Workshop",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current automated lesion segmentation algorithms are susceptible to variability in image characteristics related to MRI scanner or protocol differences. We propose a model that improves the consistency of MS lesion segmentations in inter-scanner studies. First, we train a CNN base model to approximate the performance of icobrain, an FDA-approved clinically available lesion segmentation software. A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task. Finally, the base model and the discriminator are trained adversarially on multi-scanner longitudinal data to improve the inter-scanner consistency of the base model. The performance of the models is evaluated on an unseen dataset containing manual delineations. The inter-scanner variability is evaluated on test-retest data, where the adversarial network produces improved results over the base model and the FDA-approved solution.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-02-03T16:56:05.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "inter-scanner ms lesion segmentation",
      "longitudinal data",
      "base model",
      "automated lesion segmentation algorithms",
      "adversarial training"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}