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Uncertainty-driven Forest Predictors for Vertebra Localization and Segmentation
- Source :
- Medical Image Computing and Computer Assisted Intervention (MICCAI), Lecture Notes in Computer Science ISBN: 9783319245522, MICCAI (1)
- Publication Year :
- 2015
- Publisher :
- Springer, 2015.
-
Abstract
- Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.
Details
- ISBN :
- 978-3-319-24552-2
- ISBNs :
- 9783319245522
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer Assisted Intervention (MICCAI), Lecture Notes in Computer Science ISBN: 9783319245522, MICCAI (1)
- Accession number :
- edsair.doi.dedup.....e2b4644c637b06ea71b417d1968a1bf3