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On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as \u201clung + space.\u201d While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end\u2010to\u2010end approach. These models directly map chest radiographs to clinician\u2010annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location\u2010sensitive.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U\u2010Net, LinkNet, or PSPNet) and two backbones (VGG\u201011 or MobileOne\u2010S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper\u2010parameter selection to validate the stability of our method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location\u2010specific nature of pneumothorax to enhance DL\u2010based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location\u2010relevant characteristics.<\/jats:p><\/jats:sec>","DOI":"10.1002\/hcs2.119","type":"journal-article","created":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T11:38:48Z","timestamp":1734262728000},"page":"456-474","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Leveraging anatomical constraints with uncertainty for pneumothorax segmentation"],"prefix":"10.1002","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2674-6068","authenticated-orcid":false,"given":"Han","family":"Yuan","sequence":"first","affiliation":[{"name":"Centre for Quantitative Medicine, Duke\u2010NUS Medical School  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics Duke University Durham North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Tuan Anh","family":"Tran","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology Singapore General Hospital  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxing","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing, Agency for Science, Technology and Research  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3610-4883","authenticated-orcid":false,"given":"Nan","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Quantitative Medicine, Duke\u2010NUS Medical School  Singapore"},{"name":"Programme in Health Services and Systems Research, Duke\u2010NUS Medical School  Singapore"},{"name":"Institute of Data Science National University of Singapore  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.10476"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM200003233421207"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1159\/000135932"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.10-2946"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101858"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211706"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.4103\/0971-6203.58777"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.824822"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/s1361-8415(96)80007-7"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.bioeng.2.1.315"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.824224"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.996338"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103980"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01325-5"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3122998"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.02.018"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13273"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0273-z"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_13_22_1","unstructured":"SimonyanK ZissermanA.Very deep convolutional networks for large\u2010scale image recognition.3rd Int Conf Learn Represent ICLR 2015 Conf Track Proc.2015:1\u201314.https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_2_13_23_1","doi-asserted-by":"crossref","unstructured":"SzegedyC LiuW JiaY SermanetP ReedS AnguelovD et al.Going deeper with convolutions.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston MA USA 2015:1\u20139.https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021200190"},{"key":"e_1_2_13_25_1","unstructured":"TanM LeQVEfficientNet: rethinking model scaling for convolutional neural networks.2019.arXiv.http:\/\/arxiv.org\/abs\/1905.11946"},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2911947"},{"key":"e_1_2_13_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13393"},{"key":"e_1_2_13_28_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ. 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