Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis

dc.contributor.authorPicon, Artzai
dc.contributor.authorTerradillos, Elena
dc.contributor.authorSánchez-Peralta, Luisa F.
dc.contributor.authorMattana, Sara
dc.contributor.authorCicchi, Riccardo
dc.contributor.authorBlover, Benjamin J.
dc.contributor.authorArbide, Nagore
dc.contributor.authorVelasco, Jacques
dc.contributor.authorEtzezarraga, Mª Carmen
dc.contributor.authorPavone, Francesco S.
dc.contributor.authorGarrote, Estibaliz
dc.contributor.authorSaratxaga, Cristina L.
dc.contributor.institutionCOMPUTER_VISION
dc.contributor.institutionVISUAL
dc.contributor.institutionQuantum
dc.date.issued2022
dc.description.abstractColorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent4929861
dc.identifier.citationPicon , A , Terradillos , E , Sánchez-Peralta , L F , Mattana , S , Cicchi , R , Blover , B J , Arbide , N , Velasco , J , Etzezarraga , M C , Pavone , F S , Garrote , E & Saratxaga , C L 2022 , ' Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis ' , Journal of Pathology Informatics , vol. 13 , pp. 100012 . https://doi.org/10.1016/j.jpi.2022.100012
dc.identifier.doi10.1016/j.jpi.2022.100012
dc.identifier.issn2229-5089
dc.identifier.otherresearchoutputwizard: 11556/1337
dc.language.isoeng
dc.relation.ispartofJournal of Pathology Informatics
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsMultiphoton Microscopy (MPM)
dc.subject.keywordsDataset
dc.subject.keywordsOptical Biopsy
dc.subject.keywordsConvolutional Neural Network (CNN)
dc.subject.keywordsColorectal Polyps
dc.subject.keywordsMultiphoton Microscopy (MPM)
dc.subject.keywordsDataset
dc.subject.keywordsOptical Biopsy
dc.subject.keywordsConvolutional Neural Network (CNN)
dc.subject.keywordsColorectal Polyps
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.subject.keywordsProject ID
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision Support/PICCOLO
dc.subject.keywordsinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision Support/PICCOLO
dc.subject.keywordsFunding Info
dc.subject.keywordsThis work was supported by the PICCOLO project. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein._x000D_This research has also been supported by the project ONKOTOOLS (KK2020/00069) funded by the Basque Government Industry Department under the ELKARTEK program.
dc.subject.keywordsThis work was supported by the PICCOLO project. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein._x000D_ This research has also been supported by the project ONKOTOOLS (KK2020/00069) funded by the Basque Government Industry Department under the ELKARTEK program.
dc.titleNovel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosisen
dc.typejournal article
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