Linking Whole-Slide Microscope Images with DICOM by Using JPEG2000 Interactive Protocol

Tuominen, Vilppu J.; Isola, Jorma

doi:10.1007/s10278-009-9200-1

Cited by 41 publications

(34 citation statements)

References 16 publications

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“…Each region was saved into a separate file first in uncompressed BigTiff format using a tile size of 1,024 3 1,024, followed by conversion into JPEG2000 format with a compression ratio of 50 using the JP2 WSI Converter (28). Regions of the WSIs corresponding to each bounding box were then retrieved at full resolution.…”

Section: Data Handling and Storagementioning

confidence: 99%

Metastasis detection from whole slide images using local features and random forests

et al. 2017

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Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. © 2017 International Society for Advancement of Cytometry.

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Section: Data Handling and Storagementioning

confidence: 99%

Metastasis detection from whole slide images using local features and random forests

et al. 2017

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“…B. Zoomify (www.zoomify.com) wird das nichtproprietäre JPEG2000-Format verwendet. Die Vorteile von JPEG2000 gegenüber JPEG sind bereits in vorangegangenen Publikationen beschrieben worden [6][7][8][9][10][11]. Tuominen u. Isola [8] haben die höhere Leistungsfähigkeit eines JPEG2000-Servers im direkten Vergleich zu einem JPEG-basierten System (Zoomify) nachgewiesen.…”

Section: Diskussionunclassified

Pathowiki

Roßner

Zwönitzer³

et al. 2012

Pathologe

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The project Pathowiki (www.pathowiki.org) is a free expert database for texts, images, virtual slides and links to all subject areas of pathology in the internet. The aim of this project is to integrate all available information and media, in particular virtual microscopy, to achieve a fast overview of a relevant subject area. Here we present the project’s basic functions and applications and evaluate the project with respect to the ongoing digital developments in pathology.

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“…PACS provides standard archival, retrieval and communication procedures for digital images [28], and they should follow the standards defined by Digital Imaging and Communications in Medicine (DICOM) [7,25], which are also available for pathology, although, so far, only few publications on experience have become available [38,39,40,41]. Test reports created and stored in a pathology PACS are subject to the same regulatory requirements as those created and stored in LIS [28].…”

Section: Technical Recommendations For Digital Pathology Systemsmentioning

confidence: 99%

International Clinical Guidelines for the Adoption of Digital Pathology: A Review of Technical Aspects

García-Rojo¹

2016

Pathobiology

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Digital slides, also called whole-slide images, are being evaluated to replace conventional microscopy, and several guidelines have been published. This paper reviews technical specifications of digital pathology systems that have been included in the guidelines and position papers from the Canadian Association of Pathologists, the College of American Pathologists, the American Telemedicine Association, the Digital Pathology Association, the Food and Drug Administration, the Centers for Medicare and Medicaid Services, the Centers for Disease Control and Prevention, the Society of Toxicologic Pathology, the European Commission, the Spanish Society of Anatomic Pathology, The Royal College of Pathologists and The Royal College of Pathologists of Australasia. In conclusion, most technical aspects are well covered by these guidelines, although they offer limited information regarding image quality and compression, and file formats.

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Linking Whole-Slide Microscope Images with DICOM by Using JPEG2000 Interactive Protocol

Cited by 41 publications

References 16 publications

Metastasis detection from whole slide images using local features and random forests

Metastasis detection from whole slide images using local features and random forests

Pathowiki

International Clinical Guidelines for the Adoption of Digital Pathology: A Review of Technical Aspects

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