Journal article
An approach for reducing the error rate in automated lung segmentation
Computers in biology and medicine, Vol.76, pp.143-153
09/01/2016
DOI: 10.1016/j.compbiomed.2016.06.022
PMCID: PMC5007179
PMID: 27447897
Abstract
Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual input segmentations. As basis for the fusion approach, lung segmentations generated with a region growing and model-based approach were utilized. The fusion result was generated by comparing input segmentations and selectively combining them using a trained classification system. The method was evaluated on a diverse set of 204 CT scans of normal and diseased lungs. The fusion approach resulted in a Dice coefficient of 0.9855±0.0106 and showed a statistically significant improvement compared to both input segmentation methods. In addition, the failure rate at different segmentation accuracy levels was assessed. For example, when requiring that lung segmentations must have a Dice coefficient of better than 0.97, the fusion approach had a failure rate of 6.13%. In contrast, the failure rate for region growing and model-based methods was 18.14% and 15.69%, respectively. Therefore, the proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis of lungs. Also, to enable a comparison with other methods, results on the LOLA11 challenge test set are reported.
Details
- Title: Subtitle
- An approach for reducing the error rate in automated lung segmentation
- Creators
- Gurman Gill - University of IowaReinhard R Beichel - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Computers in biology and medicine, Vol.76, pp.143-153
- DOI
- 10.1016/j.compbiomed.2016.06.022
- PMID
- 27447897
- PMCID
- PMC5007179
- NLM abbreviation
- Comput Biol Med
- ISSN
- 0010-4825
- eISSN
- 1879-0534
- Publisher
- Elsevier Ltd
- Grant note
- DOI: 10.13039/100000002, name: NIH, award: R01HL111453
- Language
- English
- Date published
- 09/01/2016
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197097602771
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