A vertebral compression fracture score based on deep generative contextual modelingDownload PDF

20 Apr 2022, 12:10 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: spine, compression fractures, generative model, inpainting, anomaly detection
  • TL;DR: This paper proposes a novel automatic method to express the degree of abnormality of the three-dimensional shape of the vertebral body.
  • Abstract: Grading of vertebral compression fractures most commonly relies on a semi-quantitative grading scale that defines fractures as height loss of the vertebral body. This paper presents an alternative approach that instead considers the three-dimensional shape of the vertebral body and expresses the abnormality of the shape on a scale from 0 to 100. The abnormality is expressed relative to the expected healthy shape, which is predicted by a deep generative model that is provided with contextual information, namely shape and orientation of surrounding vertebrae.
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Detection and Diagnosis
  • Secondary Subject Area: Segmentation
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
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