{
  "id": "2011.08761",
  "version": "v1",
  "published": "2020-11-17T16:41:31.000Z",
  "updated": "2020-11-17T16:41:31.000Z",
  "title": "Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks",
  "authors": [
    "Ke Zhang",
    "Xiahai Zhuang"
  ],
  "comment": "10 pages, 2 figures, to be published in STACOM 2020 (MICCAI Workshop)",
  "categories": [
    "cs.CV"
  ],
  "abstract": "In this paper, we study the problem of imaging orientation in cardiac MRI, and propose a framework to categorize the orientation for recognition and standardization via deep neural networks. The method uses a new multi-tasking strategy, where both the tasks of cardiac segmentation and orientation recognition are simultaneously achieved. For multiple sequences and modalities of MRI, we propose a transfer learning strategy, which adapts our proposed model from a single modality to multiple modalities. We embed the orientation recognition network in a Cardiac MRI Orientation Adjust Tool, i.e., CMRadjustNet. We implemented two versions of CMRadjustNet, including a user-interface (UI) software, and a command-line tool. The former version supports MRI image visualization, orientation prediction, adjustment, and storage operations; and the latter version enables the batch operations. The source code, neural network models and tools have been released and open via https://zmiclab.github.io/projects.html.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-17T16:41:31.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.4.6"
    ],
    "keywords": [
      "deep neural networks",
      "recognition",
      "cardiac mri orientation adjust tool",
      "standardization",
      "multi-tasking learning"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}