Deep Learning within a DICOM WSI Viewer for Histopathology

Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing....

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Authors: Vallez Enano, Noelia, Espinosa Aranda, José Luis, Pedraza Dorado, Aníbal, Déniz Suárez, Óscar, Bueno García, María Gloria
Format: article
Publication Date:2023
Country:España
Institution:Universidad de Castilla-La Mancha
Repository:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/45787
Online Access:https://doi.org/10.3390/app13179527
https://www.mdpi.com/2076-3417/13/17/9527
https://hdl.handle.net/10578/45787
Access Level:Open access
Keyword:artificial intelligence on DICOM WSI
CAD in histopathology
DICOM web viewer
DICOM WSI
digital pathology
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spelling Deep Learning within a DICOM WSI Viewer for HistopathologyVallez Enano, NoeliaEspinosa Aranda, José LuisPedraza Dorado, AníbalDéniz Suárez, ÓscarBueno García, María Gloriaartificial intelligence on DICOM WSICAD in histopathologyDICOM web viewerDICOM WSIdigital pathologyMicroscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.MDPI202520252023info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.3390/app13179527https://www.mdpi.com/2076-3417/13/17/9527https://hdl.handle.net/10578/45787reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésPID2021-127567NB-I00info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/457872026-05-27T07:36:41Z
dc.title.none.fl_str_mv Deep Learning within a DICOM WSI Viewer for Histopathology
title Deep Learning within a DICOM WSI Viewer for Histopathology
spellingShingle Deep Learning within a DICOM WSI Viewer for Histopathology
Vallez Enano, Noelia
artificial intelligence on DICOM WSI
CAD in histopathology
DICOM web viewer
DICOM WSI
digital pathology
title_short Deep Learning within a DICOM WSI Viewer for Histopathology
title_full Deep Learning within a DICOM WSI Viewer for Histopathology
title_fullStr Deep Learning within a DICOM WSI Viewer for Histopathology
title_full_unstemmed Deep Learning within a DICOM WSI Viewer for Histopathology
title_sort Deep Learning within a DICOM WSI Viewer for Histopathology
dc.creator.none.fl_str_mv Vallez Enano, Noelia
Espinosa Aranda, José Luis
Pedraza Dorado, Aníbal
Déniz Suárez, Óscar
Bueno García, María Gloria
author Vallez Enano, Noelia
author_facet Vallez Enano, Noelia
Espinosa Aranda, José Luis
Pedraza Dorado, Aníbal
Déniz Suárez, Óscar
Bueno García, María Gloria
author_role author
author2 Espinosa Aranda, José Luis
Pedraza Dorado, Aníbal
Déniz Suárez, Óscar
Bueno García, María Gloria
author2_role author
author
author
author
dc.subject.none.fl_str_mv artificial intelligence on DICOM WSI
CAD in histopathology
DICOM web viewer
DICOM WSI
digital pathology
topic artificial intelligence on DICOM WSI
CAD in histopathology
DICOM web viewer
DICOM WSI
digital pathology
description Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.3390/app13179527
https://www.mdpi.com/2076-3417/13/17/9527
https://hdl.handle.net/10578/45787
url https://doi.org/10.3390/app13179527
https://www.mdpi.com/2076-3417/13/17/9527
https://hdl.handle.net/10578/45787
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2021-127567NB-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
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