{
  "id": "1609.05396",
  "version": "v1",
  "published": "2016-09-17T21:46:21.000Z",
  "updated": "2016-09-17T21:46:21.000Z",
  "title": "A Deep Metric for Multimodal Registration",
  "authors": [
    "Martin Simonovsky",
    "Benjamín Gutiérrez-Becker",
    "Diana Mateus",
    "Nassir Navab",
    "Nikos Komodakis"
  ],
  "comment": "Accepted to MICCAI 2016; extended version",
  "categories": [
    "cs.CV",
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-09-17T21:46:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multimodal registration",
      "deep metric",
      "convolutional neural network",
      "right similarity measure",
      "outperform mutual information"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}