@inproceedings{
 title = {Pulmonary lobe segmentation in CT images using alpha-expansion},
 type = {inproceedings},
 year = {2018},
 keywords = {Alpha-expansion,Discrete optimization,Graph cuts,Lung lobe segmentation},
 pages = {387-394},
 volume = {4},
 id = {5a21d8e9-0429-3a87-aa12-5f1597d553ed},
 created = {2018-06-10T01:37:45.608Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2019-11-08T01:43:59.111Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Giuliani2018a},
 folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
 private_publication = {false},
 abstract = {Fully-automatic lung lobe segmentation in pathological lungs is still a challenging task. A new approach for automatic lung lobe segmentation is presented based on airways, vessels, fissures and prior knowledge on lobar shape. The anatomical information and prior knowledge are combined into an energy equation, which is minimized via graph cuts to yield an optimal segmentation. The algorithm is quantitatively validated on an in-house dataset of 25 scans and on the LObe and Lung Analysis 2011 (LOLA11) dataset, which contains a range of different challenging lungs (total of 55) with respect to lobe segmentation. Both experiments achieved solid results including a median absolute distance from manually set fissure markers of 1.04mm (interquartile range: 0.88-1.09mm) on the in-house dataset and a score of 0.866 on the LOLA11 dataset. We conclude that our proposed method is robust even in case of pathologies.},
 bibtype = {inproceedings},
 author = {Giuliani, Nicola and Payer, Christian and Pienn, Michael and Olschewski, Horst and Urschler, Martin},
 doi = {10.5220/0006624103870394},
 booktitle = {VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}
}

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