dc.contributor.author | Picón Ruiz, Artzai ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Terradillos Fernández, Elena | |
dc.contributor.author | Sánchez Peralta, Luisa F. | |
dc.contributor.author | Mattana, Sara | |
dc.contributor.author | Cicchi, Riccardo | |
dc.contributor.author | Blover, Benjamin J. | |
dc.contributor.author | Arbide del Río, Nagore | |
dc.contributor.author | Velasco Arteche, Jacques | |
dc.contributor.author | Etxezarraga Zuluaga, María Carmen | |
dc.contributor.author | Pavone, Francesco S. | |
dc.contributor.author | Garrote Contreras, Estíbaliz | |
dc.contributor.author | López Saratxaga, Cristina | |
dc.date.accessioned | 2022-05-25T12:10:04Z | |
dc.date.available | 2022-05-25T12:10:04Z | |
dc.date.issued | 2022-02-07 | |
dc.identifier.citation | Journal of Pathology Informatics 13 : (2022) // Article ID 100012 | es_ES |
dc.identifier.issn | 2229-5089 | |
dc.identifier.uri | http://hdl.handle.net/10810/56730 | |
dc.description.abstract | Colorectal 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 m/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. | es_ES |
dc.description.sponsorship | This 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 re-
sponsibility 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 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/732111 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | colorectal polyps | es_ES |
dc.subject | dataset | es_ES |
dc.subject | convolutional neural network (CNN) | es_ES |
dc.subject | multiphoton microscopy (MPM) | es_ES |
dc.subject | optical biopsy | es_ES |
dc.title | Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | 2022 The Author(s). Published by Elsevier Inc. on behalf of Association for Pathology Informatics. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2153353922000128?via%3Dihub | es_ES |
dc.identifier.doi | 10.1016/j.jpi.2022.100012 | |
dc.contributor.funder | European Commission | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |