Dicomization of LSM fluorescence composite microscopic image with its bioimaging information

Gupta, Yubraj; Costa, Carlos; Pinho, Eduardo; Silva, Luís A. Bastião; Mandal, Shibarjun; Neugebauer, Ute

doi:10.1109/cbms52027.2021.00086

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…In terms of image conversion, proprietary files of FIB-SEM and CLSM imaging modalities [ 17 , 18 ] were first sent to the OME-bioformats library, which then chose an appropriate image reader depending on the provided sample. If the OME-bioformats library recognizes the files, it will read the image pixel data and the metadata of the given sample.…”

Section: Overview Of the Proposed Frameworkmentioning

confidence: 99%

“…In this paper, we offer a conversion pipeline based on the standard DICOM environment that can efficiently convert several microscope imaging modalities from different scanners into the standard DICOM from proprietary imaging file formats that were gathered from confocal laser scanner microscope (CLSM), whole side imaging (WSI), and focused ion beam scanning electron microscopes (FIB-SEM) [ 17 , 18 , 19 ]. Later for validation reasons, the system was connected with the Dicoogle open-source PACS [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

Gupta

Costa

Pinho³

et al. 2022

Sensors

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The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system’s interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive.

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Section: Overview Of the Proposed Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

Gupta

Costa

Pinho³

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among other data management components, it consists of a PACS to store the WSI pictures and a DICOM-based web viewer. Recent research has proposed an automated DICOMization pipeline that can efficiently convert distinct proprietary microscope images from WSI scanners into standard DICOM with their biological information preserved in their metadata [10,11], as well DICOM WSI visualization [12,13] and other efficient architectures for telemedicine [14]. However, none of these works have demonstrated integration with both PACS and AI models yet.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning within a DICOM WSI Viewer for Histopathology

Vallez,

Espinosa-Aranda,

Pedraza

et al. 2023

Applied Sciences

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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.

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Dicomization of LSM fluorescence composite microscopic image with its bioimaging information

Cited by 2 publications

References 15 publications

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

Deep Learning within a DICOM WSI Viewer for Histopathology

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