{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:31:24Z","timestamp":1790573484025,"version":"4.1.0"},"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["NS\/A000069\/1"],"award-info":[{"award-number":["NS\/A000069\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["NS\/A000069\/1"],"award-info":[{"award-number":["NS\/A000069\/1"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric atrophy (GA) and gastric intestinal metaplasia (IM) of the mucosa of the stomach have been found to increase the risk of gastric cancer and are considered precancerous lesions. Therefore, the early detection of GA and IM may have a valuable role in histopathological risk assessment. However, GA and IM are difficult to confirm endoscopically and, following the Sydney protocol, their diagnosis depends on the analysis of glandular morphology and on the identification of at least one well-defined goblet cell in a set of hematoxylin<\/jats:ext-link> and eosin (H&E) -stained biopsy samples. To this end, the precise segmentation and classification of glands from the histological images plays an important role in the diagnostic confirmation of GA and IM. In this paper, we propose a digital pathology end-to-end workflow for gastric gland segmentation and classification for the analysis of gastric tissues. The proposed GAGL-VTNet, initially, extracts both global and local features combining multi-scale feature maps for the segmentation of glands and, subsequently, it adopts a vision transformer that exploits the visual dependences of the segmented glands towards their classification. For the analysis of gastric tissues, segmentation of mucosa is performed through an unsupervised model combining energy minimization and a U-Net model. Then, features of the segmented glands and mucosa are extracted and analyzed. To evaluate the efficiency of the proposed methodology we created the GAGL dataset consisting of 85 WSI, collected from 20 patients. The results demonstrate the existence of significant differences of the extracted features between normal, GA and IM cases. The proposed approach for gland and mucosa segmentation achieves an object dice score equal to 0.908 and 0.967 respectively, while for the classification of glands it achieves an F1 score equal to 0.94 showing great potential for the automated quantification and analysis of gastric biopsies.<\/jats:p>","DOI":"10.1371\/journal.pone.0275232","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T18:59:30Z","timestamp":1672426770000},"page":"e0275232","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":23,"title":["A digital pathology workflow for the segmentation and classification of gastric glands: Study of gastric atrophy and intestinal metaplasia cases"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0264-7943","authenticated-orcid":true,"given":"Panagiotis","family":"Barmpoutis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Waddingham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Ross","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamzeh","family":"Kayhanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Stathaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C.","family":"Alexander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marnix","family":"Jansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"issue":"4","key":"pone.0275232.ref001","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1136\/flgastro-2018-101089","article-title":"Recent advances in the detection and management of early gastric cancer and its precursors","volume":"12","author":"W Waddingham","year":"2021","journal-title":"Frontline Gastroenterology"},{"issue":"1","key":"pone.0275232.ref002","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/sj.bjc.6605993","article-title":"Salt intake and gastric cancer risk according to Helicobacter pylori infection, smoking, tumour site and histological type","volume":"104","author":"B Peleteiro","year":"2011","journal-title":"British journal of cancer"},{"issue":"2","key":"pone.0275232.ref003","first-page":"92","article-title":"Overview of current concepts in gastric intestinal metaplasia and gastric cancer","volume":"14","author":"DS Jencks","year":"2018","journal-title":"Gastroenterology & hepatology"},{"issue":"2","key":"pone.0275232.ref004","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1111\/j.1440-1746.2008.05774.x","article-title":"Intestinal metaplasia: a premalignant lesion involved in gastric carcinogenesis","volume":"24","author":"RA Busuttil","year":"2009","journal-title":"Journal of gastroenterology and hepatology"},{"issue":"Suppl 8","key":"pone.0275232.ref005","first-page":"93","article-title":"From Sidney to OLGA: an overview of atrophic gastritis.","volume":"89","author":"C Pellegrino","year":"2018","journal-title":"Acta Bio Medica: Atenei Parmensis."},{"issue":"10","key":"pone.0275232.ref006","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1097\/00000478-199610000-00001","article-title":"Classification and grading of gastritis: the updated Sydney system.","volume":"20","author":"MF Dixon","year":"1996","journal-title":"The American journal of surgical pathology"},{"issue":"5","key":"pone.0275232.ref007","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1136\/gut.2006.106666","article-title":"Gastritis staging in clinical practice: the OLGA staging system","volume":"56","author":"M Rugge","year":"2007","journal-title":"Gut"},{"issue":"1","key":"pone.0275232.ref008","doi-asserted-by":"crossref","first-page":"1927469","DOI":"10.1080\/23311916.2021.1927469","article-title":"Content-based image retrieval: A review of recent trends.","volume":"8","author":"IM Hameed","year":"2021","journal-title":"Cogent Engineering."},{"key":"pone.0275232.ref009","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: Challenges and opportunities","volume":"33","author":"A Madabhushi","year":"2016","journal-title":"Medical image analysis"},{"key":"pone.0275232.ref010","first-page":"01","article-title":"Three-dimensional tumour microenvironment reconstruction and tumour-immune interactions\u2019 analysis.","author":"P Barmpoutis","year":"2021","journal-title":"In2021 Digital Image Computing: Techniques and Applications (DICTA)"},{"key":"pone.0275232.ref011","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.bspc.2019.04.003","article-title":"Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space.","volume":"52","author":"P Barmpoutis","year":"2019","journal-title":"Biomedical Signal Processing and Control."},{"issue":"9","key":"pone.0275232.ref012","doi-asserted-by":"crossref","first-page":"e0185110","DOI":"10.1371\/journal.pone.0185110","article-title":"Grading of invasive breast carcinoma through Grassmannian VLAD encoding.","volume":"12","author":"K Dimitropoulos","year":"2017","journal-title":"PloS one."},{"issue":"9","key":"pone.0275232.ref013","doi-asserted-by":"crossref","first-page":"e0256907","DOI":"10.1371\/journal.pone.0256907","article-title":"Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.","volume":"16","author":"P Barmpoutis","year":"2021","journal-title":"Plos one"},{"key":"pone.0275232.ref014","first-page":"16519","article-title":"Bottleneck transformers for visual recognition","author":"A Srinivas","year":"2021","journal-title":"In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition"},{"key":"pone.0275232.ref015","doi-asserted-by":"crossref","unstructured":"Gao Z, Hong B, Zhang X, Li Y, Jia C, Wu J, et al. Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2021 Sep 27 (pp. 299\u2013308). Springer, Cham.","DOI":"10.1007\/978-3-030-87237-3_29"},{"issue":"1","key":"pone.0275232.ref016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2009.09.001","article-title":"Automatic segmentation of colon glands using object-graphs","volume":"14","author":"C Gunduz-Demir","year":"2010","journal-title":"Medical image analysis"},{"key":"pone.0275232.ref017","doi-asserted-by":"crossref","unstructured":"Nguyen K, Sarkar A, Jain AK. Structure and context in prostatic gland segmentation and classification. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2012 Oct 1 (pp. 115\u2013123). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-33415-3_15"},{"issue":"3","key":"pone.0275232.ref018","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1111\/j.1365-2818.2005.01531.x","article-title":"Segmentation of intestinal gland images with iterative region growing","volume":"220","author":"HS Wu","year":"2005","journal-title":"Journal of Microscopy"},{"issue":"11","key":"pone.0275232.ref019","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1109\/TMI.2015.2433900","article-title":"A stochastic polygons model for glandular structures in colon histology images","volume":"34","author":"K Sirinukunwattana","year":"2015","journal-title":"IEEE transactions on medical imaging"},{"key":"pone.0275232.ref020","unstructured":"Sorbonne Universit\u00e9s, UPMC Univ Paris 06, CNRS, INSERM, Biomedical Imaging Laboratory (LIB), Paris, France."},{"key":"pone.0275232.ref021","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","article-title":"Gland segmentation in colon histology images: The glas challenge contest","volume":"35","author":"K Sirinukunwattana","year":"2017","journal-title":"Medical image analysis"},{"key":"pone.0275232.ref022","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.media.2016.11.004","article-title":"DCAN: Deep contour-aware networks for object instance segmentation from histology images","volume":"36","author":"H Chen","year":"2017","journal-title":"Medical image analysis"},{"issue":"12","key":"pone.0275232.ref023","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1109\/TBME.2017.2686418","article-title":"Gland instance segmentation using deep multichannel neural networks","volume":"64","author":"Y Xu","year":"2017","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"pone.0275232.ref024","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.media.2018.12.001","article-title":"MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images","volume":"52","author":"S Graham","year":"2019","journal-title":"Medical image analysis"},{"key":"pone.0275232.ref025","doi-asserted-by":"crossref","unstructured":"Yan Z, Yang X, Cheng KT. A deep model with shape-preserving loss for gland instance segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2018 Sep 16 (pp. 138\u2013146). Springer, Cham.","DOI":"10.1007\/978-3-030-00934-2_16"},{"key":"pone.0275232.ref026","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neucom.2019.10.097","article-title":"Multi-scale fully convolutional network for gland segmentation using three-class classification.","volume":"380","author":"H Ding","year":"2020","journal-title":"Neurocomputing"},{"issue":"5","key":"pone.0275232.ref027","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: the path to the clinic","volume":"27","author":"J Van der Laak","year":"2021","journal-title":"Nature medicine"},{"key":"pone.0275232.ref028","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P. Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision 2017 (pp. 2980\u20132988).","DOI":"10.1109\/ICCV.2017.324"},{"key":"pone.0275232.ref029","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"A Tarvainen","year":"2017","journal-title":"Advances in neural information processing systems"},{"issue":"1","key":"pone.0275232.ref030","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s11760-016-0913-6","article-title":"Automated detection and classification of nuclei in pax5 and H&E-stained tissue sections of follicular lymphoma","volume":"11","author":"K Dimitropoulos","year":"2017","journal-title":"Signal, Image and Video Processing"},{"key":"pone.0275232.ref031","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2015 (pp. 3431\u20133440).","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"12","key":"pone.0275232.ref032","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"V Badrinarayanan","year":"2017","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"4","key":"pone.0275232.ref033","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"LC Chen","year":"2017","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"1","key":"pone.0275232.ref034","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TMI.2017.2750210","article-title":"Structure prediction for gland segmentation with hand-crafted and deep convolutional features","volume":"37","author":"S Manivannan","year":"2017","journal-title":"IEEE transactions on medical imaging"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0275232","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T19:00:16Z","timestamp":1672426816000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0275232"}},"subtitle":[],"editor":[{"given":"Ashwani","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,12,30]]},"references-count":34,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12,30]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0275232","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,30]]}}}