Conference proceeding
Multi-scale segmentation using deep graph cuts: Robust lung tumor delineation in MVCBCT
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.514-518
04/2018
DOI: 10.1109/ISBI.2018.8363628
PMCID: PMC6878112
PMID: 31772718
Abstract
Deep networks have been used in a growing trend in medical image analysis with the remarkable progress in deep learning. In this paper, we formulate the multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. The performance of the proposed method is assessed on the application of lung tumor segmentation in 38 mega-voltage cone-beam computed tomography datasets.
Details
- Title: Subtitle
- Multi-scale segmentation using deep graph cuts: Robust lung tumor delineation in MVCBCT
- Creators
- Xiaodong Wu - University of IowaZisha Zhong - University of IowaJohn Buatti - University of IowaJunjie Bai - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.514-518
- DOI
- 10.1109/ISBI.2018.8363628
- PMID
- 31772718
- PMCID
- PMC6878112
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2018
- Academic Unit
- Electrical and Computer Engineering; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Neurosurgery; Otolaryngology
- Record Identifier
- 9984197318802771
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