HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images
<p>We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513550738784256 |
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| author | Zahoor Ahmad (358103) |
| author2 | Khaled Al-Thelaya (17302711) Mahmood Alzubaidi (15740693) Faaiz Joad (21015119) Nauman Ullah Gilal (17302714) William Mifsud (1394) Sabri Boughorbel (846228) Giovanni Pintore (17302717) Enrico Gobbetti (17302720) Jens Schneider (16885948) Marco Agus (8032898) |
| author2_role | author author author author author author author author author author |
| author_facet | Zahoor Ahmad (358103) Khaled Al-Thelaya (17302711) Mahmood Alzubaidi (15740693) Faaiz Joad (21015119) Nauman Ullah Gilal (17302714) William Mifsud (1394) Sabri Boughorbel (846228) Giovanni Pintore (17302717) Enrico Gobbetti (17302720) Jens Schneider (16885948) Marco Agus (8032898) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zahoor Ahmad (358103) Khaled Al-Thelaya (17302711) Mahmood Alzubaidi (15740693) Faaiz Joad (21015119) Nauman Ullah Gilal (17302714) William Mifsud (1394) Sabri Boughorbel (846228) Giovanni Pintore (17302717) Enrico Gobbetti (17302720) Jens Schneider (16885948) Marco Agus (8032898) |
| dc.date.none.fl_str_mv | 2025-03-21T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2025.109991 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/HistoMSC_Density_and_topology_analysis_for_AI-based_visual_annotation_of_histopathology_whole_slide_images/28748321 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Histopathology AI-based annotation Nuclei localization Kernel density estimation Morse–Smale complex |
| dc.title.none.fl_str_mv | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images.</p><h2>Other Information</h2> <p> Published in: Computers in Biology and Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2025.109991" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.109991</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_127fb2df4c09073e1a7e1b50cd0998af |
| identifier_str_mv | 10.1016/j.compbiomed.2025.109991 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28748321 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide imagesZahoor Ahmad (358103)Khaled Al-Thelaya (17302711)Mahmood Alzubaidi (15740693)Faaiz Joad (21015119)Nauman Ullah Gilal (17302714)William Mifsud (1394)Sabri Boughorbel (846228)Giovanni Pintore (17302717)Enrico Gobbetti (17302720)Jens Schneider (16885948)Marco Agus (8032898)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationHistopathologyAI-based annotationNuclei localizationKernel density estimationMorse–Smale complex<p>We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images.</p><h2>Other Information</h2> <p> Published in: Computers in Biology and Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2025.109991" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.109991</a></p>2025-03-21T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2025.109991https://figshare.com/articles/journal_contribution/HistoMSC_Density_and_topology_analysis_for_AI-based_visual_annotation_of_histopathology_whole_slide_images/28748321CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287483212025-03-21T09:00:00Z |
| spellingShingle | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images Zahoor Ahmad (358103) Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Histopathology AI-based annotation Nuclei localization Kernel density estimation Morse–Smale complex |
| status_str | publishedVersion |
| title | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| title_full | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| title_fullStr | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| title_full_unstemmed | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| title_short | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| title_sort | HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Computer vision and multimedia computation Histopathology AI-based annotation Nuclei localization Kernel density estimation Morse–Smale complex |