CytometryML is an effort to produce a set of XML schemas to define Cytometry data. This is an open effort, and we appreciate your help.
Ideally, all of the groups and societies whose work is relevant to Cytometry should join together to produce one standard. Unfortunately, this may not be possible in the near future. However, these societies and groups should, at least, try to maximize interoperability by using the same data-types.
The data-types in CytometryML have been reused from the Digital Imaging and Communications in Medicine (DICOM) standard, Flow Cytometry Standard (FCS), and other standards. It has been possible, as shown in the CytometryML schemas, to employ the same standard to describe Flow and Image Cytometry. In fact, both a flow cytometer and a digital microscope were derived by restriction from a generic cytometry instrument.
The DICOM hierarchy of patient, study, series, and instance has served as the basis of the overall design of CytometryML. XML series and instance schemas have been created and have been used to generate XML pages for both flow cytometry and digital microscopy. These files have been included in this release (Please see below.)
CytometryML consists of group of schemas, which are specific to CytometryML and includes some general utility schemas, which can be used for other applications. The CytometryML schemas are available at no cost. If you have any problems with or suggestions for these schemas, please contact Robert C. Leif, email:email@example.com Please include the word CytometryML in the Subject line.
The attached miflowcyt.zip file MIFlowCyt Schemas contains 72 schema files; the total size is about 1.3 megabytes. One of the major purposes of these schemas is to describe the content of a MIFlowCyt file or section of a document. Another is to provide a description of a Cytometry experiment that is sufficient for the experiment to be repeated.
Although these schemas are still a work in progress, they may be of use as the basis of a design or reuse of the actual code. Error reporting, suggestions, collaboration, etc. would be greatly appreciated.
Analysis of the data is the subject of the present ACS software development. For instance, the CytometryML quality schema needs assistance to be extended into statistics that are relevant to Cytometry.
I have followed the principle of standards paucity by translating many DICOM entities into XML. DICOM attributes translate into XML elements. In the case of both FCS and DICOM, one or more XML attributes are been used to provide a link back to the original standard. For DICOM, the attributes correspond to the Tag and Value Representation (VR). From the DICOM DATA DICTIONARY (Vol 6), "Tag: A unique identifier for an element of information. Tags are composed of an ordered pair of numbers (a Group Number followed by an Element Number). “The Tag is the unique value that identifies every DICOM data element”
The Value Representation (VR) is the datatype or class of a DICOM element. There are only 28 VRs. Each of which is expressed as a combination of two letters. The DICOM Value Multiplicity (VM) corresponds to the permitted occurrence of an XML element. The FCS Keywords are also reproduced as XML attributes.
One significant difference between the ISAC ACS and CytometryML is that CytometryML follows the model of splitting measurements up into a series and an instance. CytometryML defines the series as those steps to the specimen prior to the specific staining of each sample and the instance is the operations on the individual samples including their specific staining and or other preparatory steps.
The description of the instrument is split into two parts. The first is the Series, which includes all of those actions or items that are common to the performance of the processing and measuring of the samples.
The second is the instance, which includes all of those items that are unique to processing of the individual aliquots of cells or particles. These collection of items that are split into individual Instances include items that have different settings, such as the optical, electronics and/ or software that are specific for the individual measurements.
ISAC_5Jun16_Poster_Code.zip This file contains, as of 5 June, 2016, the Fluophore table schema (cas.xsd) and associated schemas and a web page (cas_List21may16.xml) based upon the cas.xsd schema Validating these schemas requires the use of an XSD1.1 parser. Validating the web page includes the capability to process NVDL (Namespace-based Validation Dispatching Language. Presently cas_List21may16.xml produces the table correctly with Microsoft Edge and the version of Windows Explorer that comes with Edge. The Cytometry Metadata In Xml And Xhtml5 Cyto 2016 poster, which is also available from this web page, contains these results.
Obsolete! These schemas describe a Fluorochrome Table that contains a Chemical Abstract service (CAS) Number Fluorochrome Table as of February 5, 2016 This ZIP file contains the CAS schemas, which demonstrate the feasibility of using a cascading style sheet with elements from xhtml5 to create a table consisting of XML elements. Validating these schemas requires the use of an XSD1.1 parser that includes the capability to process NVDL (Namespace-based Validation Dispatching Language).
miflowcyt5feb16.zip This file contains, as of February 5, 2016, the MIFlowCyt schemas and a web page based upon the Experiment Overview element (instance1.xml). Validating these schemas requires the use of an XSD1.1 parser that includes the capability to process NVDL (Namespace-based Validation Dispatching Language).
MIFlowCyt.ZIP This file contains the MIFlowCyt schemas and the Experiment Overview web page, as of October 26, 2014. Validating these schemas requires the use of an XSD1.1 parser.
This ZIP file contains the CytometryML schemas pages up until 18 May 2013, These pages contain the schemas involved on relations. Validating these schemas requires the use of an XSD1.1 parser, such as Xerxes in oXygen 14.1.
Zip file of CytometryML and web pages , as of 26 January, 2013. This ZIP contains most of the CytometryML schemas including all that are involved on relations and an XML example, Relation_Image_List21Jan13.xsd. Validating these schema requires the use of an XSD1.1 parser, such as Xerxes in oXygen 14.1.
Zip file of CytometryML and web pages , as of 7 October, 2012. This ZIP contains most of the CytometryML schemas including all that are involved on relations.
Zip file of CytometryML Pages as of 28 September, 2011.
CytometryML is an XML schema based translation, extension, and amalgamation of the DICOM and ISAC standards. CytometryML consists of 5 major XML schemas: Relations, Series, Instance, Instrument, and Specimen; it also includes Image, and List-Mode schemas. Series metadata, which is specific for an entire collection of images and/or list-mode files produced by a single instrument and derived from a single specimen, is stored together with related metadata files in an EPUB container (ZIP) file. Each Instance container file includes binary image and/or list-mode files together with related metadata files that are specific for a single or closely related group of instrument runs from a single specimen. The ISAC Archival Cytometry Standard (ACS) proposed Table of Contents schema including its Resource Description Framework (RDF) capabilities has been extended, modified, and renamed for use in the Instance schema. The replacement of standard RDF syntax by a simple sentence (element) based format (Subject, Predicate, and Object) permits multiple relations between two file references that can be in both directions. Extended ISAC CYTO 2012 Poster
Robert C. Leif and Stephanie H. Leif, Cytometry metadata in XML
Introduction: The International Society for Advancement of Cytometry (ISAC) has created a standard for the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt 1.0). CytometryML will serve as a common metadata standard for flow and image cytometry (digital microscopy). Methods: The MIFlowCyt data-types were created, as is the rest of CytometryML, in the XML Schema Definition Language (XSD1.1). The datatypes are primarily based on the Flow Cytometry and the Digital Imaging and Communication (DICOM) standards. A small section of the code was formatted with standard HTML formatting elements (p, h1, h2, etc.). Results:1) The part of MIFlowCyt that describes the Experimental Overview including the specimen and substantial parts of several other major elements has been implemented as CytometryML XML schemas (www.cytometryml.org). 2) The feasibility of using MIFlowCyt to provide the combination of an overview, table of contents, and/or an index of a scientific paper or a report has been demonstrated. Previously, a sample electronic publication, EPUB, was created that could contain both MIFlowCyt metadata as well as the binary data. Conclusions: The use of CytometryML technology together with XHTML5 and CSS permits the metadata to be directly formatted and together with the binary data to be stored in an EPUB container. This will facilitate: formatting, data- mining, presentation, data verification, and inclusion in structured research, clinical, and regulatory documents, as well as demonstrate a publication’s adherence to the MIFlowCyt standard, promote interoperability and should also result in the textual and numeric data being published using web technology without any change in composition.
Robert C. Leif and Stephanie H. Leif, A shared standard for cytometry and pathology that is based upon an EPUB and includes a means to describe relation that employs elements, data structures, and templates.
Introduction: The development of cytometry standards is complicated by their being relevant to pathology and biological science, which already have standards. CytometryML, the cytometry markup language, is an XML standard for flow and image cytometry, which includes both objects and their relationships, and is based upon existing standards: the International Society for Advancement of Cytometry ( ISAC) FCS, Digital Imaging and Communication in Medicine ( DICOM), and International Digital Publishing Forum (EPUB).
Methods: The CytometryML schemas are written in XML Schema Definition (XSD1.1). Object-oriented methodology was employed to create the CytometryML schemas, which were tested by translating specific XSD elements into XML and filling in the values. The attribute based syntax description of relationships in the Resource Description Framework (RDF) has been replaced by an XSD element based implementation. The ISAC Archival Cytometry Standard (ACS) concept of a zipped data container file was further refined to be a EPUB file. Since Table of Contents information is present in an EPUB container, it was minimized in the Relations schema, which replaced the ToC schema of the ACS and includes a modified and extended version of the ToC RDF capabilities.
Results: An XML based system that includes the DICOM specified separation of series and instances and includes relationships has been created.
Conclusions: CytometryML and EPUB could be used for the transmission of research and medical data and be extension some of the pathology part of DICOM. The CytometryML version of RDF in XSD could be extended to provide XSD with full RDF capabilities.
Keywords: CytometryML, DICOM, EPUB, FCS, Series, Standard, XML Schema, RDF
Robert C. Leif and Stephanie H. Leif, A CytometryML Table of Contents that Describes Relationships between Elements based upon DICOM and Flow Cytometry Standard. The latest version of the code includes a means to provide some RDF functionality in XML schemas
CytometryML is an XML schema based translation, extension and amalgamation of the DICOM and ISAC standards. CytometryML consists of 4 major XML schemas: Series, Instance, Instrument, and Specimen; it also includes Image and List-Mode schemas. Series metadata, which is specific for an entire collection of images and/or list-mode files produced by a single instrument and derived from a single specimen, is stored together with associated metadata files in a container (ZIP) file. Each Instance container file includes binary image and/or list-mode files together with associated metadata files that are specific for a single or closely related group of instrument runs from a single specimen. The Archival Cytometry Standard (ACS) proposed Table of Contents schema including its Resource Description Framework (RDF) capabilities has been extended and modified for use in the Instance schema.
Robert C. Leif, Toward the integration of cytomics and medicine, J. Biophoton. Special Issue: Towards in vivo Flow Cytometry 2, Pages:482-493
The integration of cytomics research and healthcare informatics will facilitate technology transfer and reduce medical costs. The CytometryML prototype of the Advanced Cytometry Standard (ACS) has the benefits of including microscopic image and flow list-mode data, being based on XML and thus is compatible with existing medical and scientific informatics standards, such as HL7, and employing a design based upon the Digital Imaging and Communications in Medicine (DICOM) standard. The reuse of the well tested DICOM model resulted in a great decrease in the design and documentation effort and increased probability of reliability. Schemas for flow cytometers and microscopes have been created. XML schemas for two related types of container (ZIP) files have been specified for a set of measurements. The series and instance containers respectively include the metadata that is constant and the metadata that is specific to an individual or small closely related group of measurements. (� 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Josef Spidlen, Robert C. Leif, Wayne Moore, Mario Roederer, International Society for the Advancement of Cytometry Data Standards Task Force, Ryan R. Brinkman, Gating-ML: XML-based gating descriptions in flow cytometry, Cytometry Part A 73A, Issue 12, Date: December 2008, Pages: 1151-1157
The lack of software interoperability with respect to gating due to lack of a standardized mechanism for data exchange has traditionally been a bottleneck, preventing reproducibility of flow cytometry (FCM) data analysis and the usage of multiple analytical tools. To facilitate interoperability among FCM data analysis tools, members of the International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) have developed an XML-based mechanism to formally describe gates (Gating-ML). Gating-ML, an open specification for encoding gating, data transformations and compensation, has been adopted by the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate exchange of gating descriptions the same way that FCS facilitated for exchange of raw FCM data. Its adoption will open new collaborative opportunities as well as possibilities for advanced analyses and methods development. The ISAC DSTF is satisfied that the standard addresses the requirements for a gating exchange standard. � 2008 International Society for Advancement of Cytometry
Jamie A. Lee, Josef Spidlen, Keith Boyce, Jennifer Cai, Nicholas Crosbie, Mark Dalphin, Jeff Furlong, Maura Gasparetto, Michael Goldberg, Elizabeth M. Goralczyk, Bill Hyun, Kirstin Jansen, Tobias Kollmann, Megan Kong, Robert Leif, Shannon McWeeney, Thomas D. Moloshok, Wayne Moore, Garry Nolan, John Nolan, Janko Nikolich-Zugich, David Parrish, Barclay Purcell, Yu Qian, Biruntha Selvaraj, Clayton Smith, Olga Tchuvatkina, Anne Wertheimer, Peter Wilkinson, Christopher Wilson, James Wood, Robert Zigon, The International Society for Advancement of Cytometry Data Standards Task Force, Richard H. Scheuermann, Ryan R. Brinkman, MIFlowCyt: The minimum information about a flow cytometry experiment, Cytometry Part A 73A, Issue 10, Date: October 2008, Pages: 926-930
A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoption of MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data. � 2008 International Society for Advancement of Cytometry
R.C. Leif, S.H. Leif, S.B. Leif, CytometryML, An XML Format based on DICOM for Analytical Cytology Data Cytometry 54A pp. 56-65 (2003).
Background: Flow Cytometry Standard (FCS) was initially created to standardize software researchers use to analyze, transmit, and store data produced by flow cytometers and sorters. Because of the clinical utility of flow cytometry, it is necessary to have a standard consistent with the requirements of medical regulatory agencies.
Method:1) Extend the existing mapping of FCS to the Digital Imaging and Communications in Medicine (DICOM) standard to include list-mode data produced by flow, laser scanning cytometry, and microscopic image cytometry. FCS list-mode was mapped to the DICOM Waveform Information Object. 2) Create a collection of XML schemas to express the DICOM analytical cytology text based data-types except for large binary objects. 3) Accomplish this creation of a cytometry markup language, CytometryML, in an open environment that is subject to continuous peer review.
Results:The feasibility of expressing the data contained in FCS, including list-mode in DICOM, has been demonstrated; and a preliminary mapping for list-mode data in the form of XML Schemas and documents has been completed. DICOM permits the creation of indices that can be used to rapidly locate in a list-mode file the cells that are members of a subset. DICOM and its coding schemes for other medical standards can be represented by XML schemas, which can be combined with other relevant XML applications, such as Mathematical Markup Language (MathML).
Conclusions:The use of XML format based on
DICOM for analytical cytology has met most of the previously
specified requirements and appears capable of meeting the others;
therefore, the present FCS should be retired and replaced by an
open, XML based standard, CytometryML.
R.C. Leif Safe Software Standards and XML Schemas (preprint) in SPIE Proc. Vol. 7556 (2010).
The goal of this work is to develop a safe software construction means for an XML based data standard for a class of medical devices, cytometry instruments. Unfortunately, the amount of empirical evidence to archive this goal is minimal. Therefore, technologies associated with high reliability were employed together with reuse of existing designs.
The basis for a major part of the design was the Digital Imaging and Communications in Medicine (DICOM) standard and the Flow Cytometry Standard (FCS). Since the DICOM Standard is a Class II device, the safety of software should be maximized. The XML Schema Definition Language (XSDL) has been used to develop schemas that maximize readability, modularity, strong typing, and reuse. An instance and an instrument XML schema were created for data obtained with a microscope by importing multiple schemas that each consisted of a class that described one object. This design was checked by validating the schemas and creating XML pages from them.
R.C. Leif An XML Cytometry Standard Based on DICOM (preprint) in SPIE Proc. Vol. 7264 (2009).
Introduction: The International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) is developing a new Advanced Cytometry Specification (ACS). DICOM has developed and is extending a pathology extension. The work of both groups is complementary with some overlap. Interoperation would benefit both groups and permit each to benefit from the other�s expertise.
Methods: The design and implementation of the CytometryML version of the ACS schemas have been based on each schema describing one object (modularity), iterative (spiral) development, inheritance, and reuse of data-types and their definitions from DICOM, Flow Cytometry Standard, and other standards.
Results: These schemas have been
validated with two tools and XML pages were generated from
highest level schemas. Binary image data and its associated
metadata are stored together in a zip file based container. A
schema for a table of contents, which is one of the metadata
files of this container, has recently been developed and reported
upon. The binary image data is placed in one file in the
container; and the metadata associated with an image in another.
The schema for the image metadata file includes elements that are
based on the DICOM design. This image schema includes
descriptions of the acquisition context, image (including
information on compression), specimen, slide, transmission
medium, major optical parts, optical elements in one or more
optical channels, detectors, and pixel format. The image schema
describes both conventional camera systems and scanning or
R.C. Leif, J. Spidlen, R. R. Brinkman A Container for the Advanced Cytometry Standard (ACS) (preprint) to be in Manipulation and Analysis of Biomolecules, Cells, and Tissues V, D. Farkas, R. C. Leif, and D. V. Nicolau, Editors, SPIE Proc. Vol. 7182 (2009).
Introduction: The highest priority for the Advanced Cytometry Standard (ACS) is the interpretation of list-mode cytometry measurements. Other priorities of lesser importance are the capacity to reproduce a cytometry measurement and the implementation of a digital microscopy image standard. The sequential nature of these requirements is being accommodated by a flexible, modular design. A major feature of this modular design is the creation of a design for an Advanced Cytometry Standard Container (ACSC) that includes a Table of Contents (ToC) XML file, one or more binary data containing files and files that contain the meta-data that describes the binary data.
Methods: The design and partial implementation of the CytometryML schemas have been based on the techniques of modularity (each schema describing one object), iterative (spiral) development, inheritance, and reuse. Data-types including their definitions have been reused from DICOM, FCS, and other standards.
Results: A prototype ToC schema together with prototypes of many of the schemas that describe the contents of the ACSC have been created together with their supporting schemas. These schemas have been validated with two tools and XML pages were generated from the main element(s) of the highest level schemas. These elements describe the table of contents of the zipped container file and a flow-cytometry instrument. The zipped container file (ACSC) describes and contains the meta and binary data.
R.C. Leif, J. Spidlen, R. R. BrinkmanCytometry Standards Continuumin Manipulation and Analysis of Biomolecules, Cells, and Tissues V, D. Farkas, R. C. Leif, and D. V. Nicolau, Editors, SPIE Proc. Vol. 6859 (2008).
Introduction: The International Society for Analytical Cytology, ISAC, is developing a new combined flow and image Analytical Cytometry Standard (ACS). This standard needs to serve both the research and clinical communities. The clinical medicine and clinical research communities have a need to exchange information with hospital and other clinical information systems.
Methods: 1) Prototype the standard by creating CytometryML and a RAW format for binary data. 2) Join the ISAC Data Standards Task Force. 3) Create essential project documentation. 4) Cooperate with other groups by assisting in the preparation of the DICOM Supplement 122: Specimen Module and Pathology Service-Object Pair Classes.
Results: CytometryML has been created and
serves as a prototype and source of experience for the following:
the Analytical Cytometry Standard (ACS) 1.0, the ACS container,
Minimum Information about a Flow Cytometry Experiment
(MIFlowCyt), and Requirements for a Data File Standard Format to
Describe Flow Cytometry and Related Analytical Cytology Data.
These requirements provide a means to judge the appropriateness
of design elements and to develop tests for the final ACS. The
requirements include providing the information required for
understanding and reproducing a cytometry experiment or clinical
measurement, and for a single standard for both flow and digital
microscopic cytometry. Schemas proposed by other members of the
ISAC Data Standards Task Force (e.g, Gating-ML) have been
independently validated and have been integrated with
CytometryML. The use of netCDF as an element of the ACS container
has been proposed by others and a suggested method of its use is
R.C. Leif CytometryML, a data standard, which has been designed to interface with other standards (preprint) to be in Manipulation and Analysis of Biomolecules, Cells, and Tissues V, D. Farkas, R. C. Leif, and D. V. Nicolau, Editors, SPIE Proc. Vol. 6441 (2007).
Because of the differences in the requirements, needs, and past histories including existing standards of the creating organizations, a single encompassing cytology-pathology standard will not, in the near future, replace the multiple existing or under development standards. Except for DICOM and FCS, these standardization efforts are all based on XML. CytometryML is a collection of XML schemas, which are based on the Digital Imaging and Communications in Medicine (DICOM) and Flow Cytometry Standard (FCS) datatypes. The CytometryML schemas contain attributes that link them to the DICOM standard and FCS. Interoperability with DICOM has been facilitated by, wherever reasonable, limiting the difference between CytometryML and the previous standards to syntax. In order to permit the Resource Description Framework, RDF, to reference the CytometryML datatypes, id attributes have been added to many CytometryML elements. The Laboratory Digital Imaging Project (LDIP) Data Exchange Specification and the Flowcyt standards development effort employ RDF syntax. Documentation from DICOM has been reused in CytometryML. The unity of analytical cytology was demonstrated by deriving a microscope type and a flow cytometer type from a generic cytometry instrument type. The feasibility of incorporating the Flowcyt gating schemas into CytometryML has been demonstrated. CytometryML is being extended to include many of the new DICOM Working Group 26 datatypes, which describe patients, specimens, and analytes. In situations where multiple standards are being created, interoperability can be facilitated by employing datatypes based on a common set of semantics and building in links to standards that employ different syntax.
R.C. Leif CytometryML and other data formats in Manipulation and Analysis of Biomolecules, Cells, and Tissues III, D. Farkas, D. V. Nicolau, and R. C. Leif, Editors, SPIE Proc. Vol. 6088-0L pp. 1-7 (2006).
Cytology automation and research will be enhanced by
the creation of a common data format. This data format would
provide the pathology and research communities with a uniform way
for annotating and exchanging images, flow cytometry, and
associated data. This specification and/or standard will include
descriptions of the acquisition device, staining, the binary
representations of the image and list-mode data, the measurements
derived from the image and/or the list-mode data, and descriptors
for clinical/pathology and research. An international,
vendor-supported, non-proprietary specification will allow
pathologists, researchers, and companies to develop and use image
capture/analysis software, as well as list-mode analysis
software, without worrying about incompatibilities between
proprietary vendor formats.
Presently, efforts to create specifications and/or
descriptions of these formats include the Laboratory Digital
Imaging Project (LDIP) Data Exchange Specification; extensions to
the Digital Imaging and Communications in Medicine (DICOM); Open
Microscopy Environment (OME); Flowcyt, an extension to the
present Flow Cytometry Standard (FCS); and CytometryML.
The feasibility of creating a common data
specification for digital microscopy and flow cytometry in a
manner consistent with its use for medical devices and
interoperability with both hospital information and picture
archiving systems has been demonstrated by the creation of the
CytometryML schemas. The feasibility of creating a software
system for digital microscopy has been demonstrated by the OME.
CytometryML consists of schemas that describe instruments and
their measurements. These instruments include digital microscopes
and flow cytometers. Optical components including the
instruments� excitation and emission parts are described. The
description of the measurements made by these instruments
includes the tagged molecule, data acquisition subsystem, and the
format of the list-mode and/or image data. Many of the
CytometryML data-types are based on the Digital Imaging and
Communications in Medicine (DICOM). Binary files for images and
list-mode data have been created and read.
R.C. Leif CytometryML, Binary Data Standards Manipulation and Analysis of Biomolecules, Cells, and Tissues II, D. V. Nicolau, J. Enderlein, R. C. Leif, and D. Farkas, Editors, SPIE Proc. Vol. 5699, pp. 325-333 (2005).
CytometryML is a proposed new Analytical Cytology
(Cytomics) data standard, which is based on a common set of XML
schemas for encoding flow cytometry and digital microscopy text
based data types (metadata). CytometryML schemas reference both
DICOM (Digital Imaging and Communications in Medicine) codes and
FCS keywords. Flow Cytometry Standard (FCS) list-mode has been
mapped to the DICOM Waveform Information Object. The separation
of the large binary data objects (list mode and image data) from
the XML description of the metadata permits the metadata to be
directly displayed, analyzed, and reported with standard
commercial software packages; the direct use of XML languages;
and direct interfacing with clinical information systems. The
separation of the binary data into its own files simplifies
parsing because all extraneous header data has been eliminated.
The storage of images as two-dimensional arrays without any
extraneous data, such as in the Adobe� Photoshop� RAW format,
facilitates the development by scientists of their own analysis
and visualization software. Adobe Photoshop provided the display
infrastructure and the translation facility to interconvert
between the image data from commercial formats and RAW format.
Similarly, the storage and parsing of list mode binary data type
with a group of parameters that are specified at compilation time
is straight forward. However when the user is permitted at
run-time to select a subset of the parameters and/or specify
results of mathematical manipulations, the development of special
software was required. The use of CytometryML will permit
investigators to be able to create their own interoperable data
analysis software and to employ commercially available software
to disseminate their data.
R.C. Leif, S.H. Leif, S.B. Leif, CytometryML, a Markup Language for Analytical Cytology in Manipulation and Analysis of Biomolecules, Cells and Tissues, D. V. Nicolau, J. Enderlein, and R. C. Leif, Editors, SPIE Proc. Vol. 4962 pp 288-297 (2003).
Cytometry Markup Language, CytometryML, is a proposed new analytical cytology data standard. CytometryML is a set of XML schemas for encoding both flow cytometry and digital microscopy text based data types. CytometryML schemas reference both DICOM (Digital Imaging and Communications in Medicine) codes and FCS keywords. These schemas provide representations for the keywords in FCS 3.0 and will soon include DICOM microscopic image data. Flow Cytometry Standard (FCS) list-mode has been mapped to the DICOM Waveform Information Object. A preliminary version of a list mode binary data type, which does not presently exist in DICOM, has been designed. This binary type is required to enhance the storage and transmission of flow cytometry and digital microscopy data. Index files based on Waveform indices will be used to rapidly locate the cells present in individual subsets. DICOM has the advantage of employing standard file types, TIF and JPEG, for Digital Microscopy.
Using an XML schema based representation means that
standard commercial software packages such as Excel and MathCad
can be used to analyze, display, and store analytical cytometry
data. Furthermore, by providing one standard for both DICOM data
and analytical cytology data, it eliminates the need to create
and maintain special purpose interfaces for analytical cytology
data thereby integrating the data into the larger DICOM and other
clinical communities. A draft version of CytometryML is available
R.C. Leif and S.B. Leif, A DICOM Compatible Format for Analytical Cytology Data, that can be Expressed in XML in Optical Diagnostics of Living Cells IV, D. L. Farkas and R. C. Leif, Editors, SPIE Proc. Vol. 4260 pp. 238-48 (2001).
Flow Cytometry data can be directly mapped to the Digital Imaging and Communications in Medicine, DICOM standard. A preliminary mapping of list-mode data to the DICOM Waveform information Object will be presented. This mapping encompasses both flow and image list-mode data. Since list-mode data is also produced by digital slide microscopy, which has already been standardized under DICOM, both branches of Analytical Cytology can be united under the DICOM standard. This will result in the functionality of the present International Society for Analytical Cytology Flow Cytometry Standard, FCS, being significantly extended and the elimination of the previously reported FCS design deficiencies. Thus, The present Flow Cytometry Standard can and should be replaced by a Digital Imaging and Communications in Medicine, DICOM, standard. Expression of Analytical Cytology data in any other format, such as XML, can be made interoperable with DICOM by employing the DICOM data types. A fragment of an XML Schema has been created, which demonstrates the feasibility of expressing DICOM data types in XML syntax. The extension of DICOM to include Flow Cytometry will have the benefits of 1) retiring the present FCS, 2) providing a standard that is ubiquitous, internationally accepted, and backed by the medical profession,and 3) interoperating with the existing medical informatics infrastructure.
R.C. Leif and S.B. Leif, A DICOM Compatible Format for Analytical Cytology Data in Optical Investigations of Cells In Vitro and In Vivo, D. L. Farkas, R. C. Leif, B. J. Tromberg, Editors, A. Katzir Biomedical Optics Series Ed. Proc. of SPIE Vol. 3260, ISBN 0-8194-2699-7 pp. 282-289, (1998).
The addition of a list mode data type to the Digital
Imaging and Communications in Medicine standard, DICOM will
enhance the storage and transmission of digital microscopy data
and extend DICOM to include flow cytometry data. This would
permit the present International Society for Analytical Cytology
Flow Cytometry Standard to be retired. DICOM includes: image
graphics objects, specifications for describing: studies,
reports, the acquisition of the data, work list management, and
the individuals involved (physician, patient, etc.). The glossary
of terms (objects) suitable for use with DICOM has been extended
to include the collaborative effort of Logical Observation
Identifier Names and Codes (LOINC) and Systematized Nomenclature
of Human and Veterinary Medicine (SNOMED) to create a consistent,
unambiguous clinical reference terminology. It also appears that
DICOM will be a significant part of the Common Object Request
Broker Architecture, CORBA.
R.C. Leif and S.B. Leif, The Evolution of Flow Cytometry Standard, FCS3.0, into a DICOM Compatible Format, in Optical Diagnostics of Biological Fluids and Advanced Techniques in Analytical Cytology, Ed. A. V. Priezzhev , T. Asakura, and R. C. Leif. A. Katzir Series Editor, Progress Biomedical Optics Series , SPIE Proceedings Series, Vol. 2982, pp 354-366 (1997).
The International Society for Analytical Cytology,
ISAC,has developed a Flow Cytometry Standard (FCS) to permit data
interchange. ISAC will soon replace Flow Cytometry Standard 2.0
(FCS2.0) with FCS3.0. Unfortunately,the proposed FCS3.0 is still
fraught with problems, which are of sufficient magnitude as to
warrant its early replacement. The most reasonable replacement is
as a supplement to the Digital Imaging and Communications in
Medicine, DICOM 3.0, standard. The recent digital microscopy
extension of DICOM can be extended and modified to include flow
cytometry data. DICOM includes: image graphics objects,
specifications for describing: studies, reports,the acquisition
of the data and the individuals involved, physician, patient,
etc. Storing the present FCS data in a database, which has
already been accomplished with the QC Tracker software, will
facilitate the transition of FCS to DICOM.
Digital Imaging and Communications in Medicine, DICOM standard.
Resource Description Framework, RDF.
RDFa in XHTML: Syntax and Processing, RDFa.
Advanced Cytometry Standard (ACS) Requirements for a data file standard format to describe cytometry and related analytical cytology data, ACS ACS is now Archieval Cytometry Standard
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