Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection
Public Deposited- Creator
- Park, Sang Hyun
- Gao, Yaozong
- Shi, Yinghuan
- Shen, Dinggang
- Abstract
- Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods.
- Date of publication
- 2014
- Keyword
- Computed tomography
- e.g. blood
- Learning machines
- Segmentation
- Digital compu
- Haemocytometers
- computerised tomography
- Radiation therapy
- including brachytherapy
- learning (artificial intelligence)
- urine
- cancer
- In which a programme is changed according to experience gained by the computer itself during a complete run
- Biological material
- regression analysis
- feature selection
- Therapeutic applications
- Optimization
- radiation therapy
- Conformal radiation treatment
- image segmentation
- Computerised tomographs
- DOI
- Identifier
- PMCID: PMC4224685
- Onescience id: 3b8b8970c90db81d97c62ce62fc2a0d50b89a987
- PMID: 25370629
- Publisher DOI: https://doi.org/10.1118/1.4898200
- Resource type
- Article
- Rights statement
- In Copyright
- Journal title
- Medical Physics
- Journal volume
- 41
- Journal issue
- 11
- Language
- English
- ISSN
- 0094-2405
- 1522-8541
- 2473-4209
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