Learning and Incorporating Shape Models for Semantic Segmentation
| Autor: | Sheshadri Thiruvenkadam, Prasad Sudhakar, Vivek Vaidya, Hariharan Ravishankar, Rahul Venkataramani |
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| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Contrast (statistics) Scale-space segmentation Pattern recognition 02 engineering and technology Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business computer |
| Zdroj: | Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 ISBN: 9783319661810 MICCAI (1) |
| Popis: | Semantic segmentation has been popularly addressed using Fully convolutional networks (FCN) (e.g. U-Net) with impressive results and has been the forerunner in recent segmentation challenges. However, FCN approaches do not necessarily incorporate local geometry such as smoothness and shape, whereas traditional image analysis techniques have benefitted greatly by them in solving segmentation and tracking problems. In this work, we address the problem of incorporating shape priors within the FCN segmentation framework. We demonstrate the utility of such a shape prior in robust handling of scenarios such as loss of contrast and artifacts. Our experiments show \(\approx 5\%\) improvement over U-Net for the challenging problem of ultrasound kidney segmentation. |
| Databáze: | OpenAIRE |
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