Conference PaperPDF Available

A DICOM Standard Pipeline for Microscope Imaging Modalities

A DICOM Standard Pipeline for Microscope
Imaging Modalities
Yubraj Gupta, Carlos Costa
Department of Computer Engineering
University of Aveiro
Aveiro, Portugal,
Eduardo Pinho, Lu´
ıs A. Basti˜
ao Silva
BMD Software
Aveiro, Portugal,
Abstract—In the nineties, the adoption of the DICOM standard
format in radiology departments brought numerous advantages
to clinical practice. The setup of PACS with standard com-
munication processes and data formats allowed the creation
of central repositories, fast retrieval of images, visualization of
images acquired with several modalities, and simultaneous access
at distributed places. Nowadays, microscopy imagining faces the
same normalization challenge with the proliferation of equipment
that stores data in a proprietary format and provides dedicated
visualization software. This reality severely limits the imple-
mentation of vendor-neutral archives with common visualization
processes, conditioning the research work and its integration
in clinical environments. This paper proposed a pipeline for
the integration of multiple microscopy imaging modalities into
the PACS-DICOM universe, including the numerous metadata
elements. A proof-of-concept system was developed, for validation
purposes, and integrated with the Dicoogle open-source PACS,
providing image storage, metadata indexing and visualization.
Index Terms—Microscopy imaging, DICOM, PACS, Interop-
erability, Dicomization
Automated analysis of diverse information related to biolog-
ical structures and functions of living organisms has become
a remarkable area in biological research in which images are
one of the most relevant classes of related data. Due to the
great advances in digital technology as well as in the light or
electron microscopy field during the previous two decades,
the studies on cell biology through cellular imaging have
acquired continuously growing importance [1]. In particular,
remarkable developments that have contributed to this growth
are fluorescent probes, a beam of electron and light, and high-
resolution microscopes [2]. The role of cell microscopy in
research related to genome and proteome has gained a primary
significance, which includes studies on living cells, such as cell
phase identification, cell tracking, or tracking of subcellular
structures [3].
Although microscopy technology has proven to be a great
development in the field of life science, this area faces signif-
icant challenges in the processing and sharing of data owing
to the several digital file formats that are used [1]. Currently,
more than dozens of microscope scanners are available in
the market, using diverse digital file formats, although some
scanners working principle is similar to each other (like
compression function). For microscopy images, no universal
standard file formats have been adopted. Open-source and
commercial software solutions to read these proprietary digital
file formats were developed [4], [5]. However, these solutions
primarily provide access to image pixel data whereas crucial
metadata related to the clinical context (and the acquisition
process) remains largely inaccessible [6]. Furthermore, the
proliferation of competing vendor solutions increases the
number of proprietary formats, which represents a barrier to
interoperability and maintainability. Image analysis software
and commercial microscopy companies often engage in format
conflicts, whereas open-source solutions grapple to bridge the
gaps between many proprietary formats. Therefore, there is a
compelling demand for data standardization in the microscope
world to facilitate clinical integration and to support the
computational development streams [1].
In the nineties, digital medical imaging used in the hospital
environment faced the same interoperability problem, but the
development and adoption of the DICOM (Digital Imaging
and Communication in Medicine) standard for data storage
and communications changed this reality [7]. In the beginning,
it was necessary to create Dicomization gateways to integrate
legacy systems, converting their analog media and digital pro-
prietary formats into DICOM and integrate them into the in-
stitutional PACS (Picture Archiving Communication System)
environment (which integrates image acquisition modalities,
the image archiving system, workstation displays, and the
underlying network), facilitating the studies sharing with other
institutions for visualization or analysis [8]. Besides, storing
image pixel data, a DICOM object also supports metadata and
the communication layer of the standard provides various ser-
vices like, for instance, data storage, query/retrieve, schedul-
ing of acquisition, print management, and security profiles.
Therefore, it is rightful to say that “DICOM standardization
has completely addressed the issue of interoperability in the
field of radiology” [9].
This article proposes a DICOM standard conversion pipeline
for multiple microscopy imaging modalities. The gateway
takes different equipment data formats, as input, and converts
them into DICOM standard file formats, integrating into a
single object the study pixel data and the numerous proprietary
metadata. The solution was integrated with the Dicoogle open-
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2021 IEEE Symposium on Computers and Communications (ISCC) | 978-1-6654-2744-9/21/$31.00 ©2021 IEEE | DOI: 10.1109/ISCC53001.2021.9631529
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source PACS for validation purposes. The result was a vendor-
neutral archive able to receive studies from distinct equipment
manufactures and different microscopy modalities, that index
and validates the associated metadata, that provides web-based
visualization services, and that provides access to third-party
DICOM compliant software.
Fig. 2: FIB-SEM electron microscope image of a healthy
mouse liver tissue.
emanating from the targeted area of the sample. And, there-
fore, the surface of the specimens is then scanned by moving
either the specimens or the light beam. This allows for the 2D
image reconstruction at a given height, which is an advantage
of CLSM compared to other fluorescence microscopes. Several
images can be taken swiftly with a small portion of the
specimen viewed at a time. For this experiment, we have
obtained three CLSM images, one image from the Leibniz-
IPHT institute which belongs to mouse bone tissue (obtained
utilizing Zeiss LSM 780, AxisObserver microscope, and has
an extension of .CZI), second one, we have downloaded
from the SPARC homepage (
and belongs to the human pancreas cleared with passive
CLARITY, immunostained (which was imaged utilizing Zeiss
LSM 710 microscope scanner, and has an extension of .CZI),
and the third one, we have downloaded from cell image
library ( and belongs
to zebrafish embryo mitosis and cytokinesis (obtained utilizing
Zeiss LSM 710 microscope scanner, and has an extension of
.LSM). Figure 1 shows the samples of the CLSM image.
2) Focused Ion
Scanning Electron Microscopy (FIB-
SEM): An electron microscope allows higher magnification
for a sample by using a beam of electrons to create an
image, meaning much smaller objects can be examined in
finer detail to reveal their microstructures [12]. A typical SEM
device consists of an electron input, an electron detector and
electromagnetic lenses. Instead of light, it utilizes a single
electron beam to obtain images and x-rays to analyze the
sample. The FIB-SEM adds a second beam, ion-beam, to cut
into the material while the SEM carries out high-resolution
work. It thus allows to acquire a magnified picture of the
surface of thick specimens and to analyze their composition.
Both FIB and SEM can be used independently, but combining
them into a single layer opens a wider range of options that are
otherwise not possible. For this experiment, we have obtained
one FIB-SEM image, and it was provided by the Leibniz-
IPHT institute, it belongs to healthy mouse liver tissue, and
Fig. 1: CLSM fluorescence microscope samples: (a) Three-
channel human islet microvasculature (alpha-cells (glucagon:
visualized in blue), smooth muscle cells (smooth muscle actin:
visualized in red), and basement membranes (collagen IV:
visualized in green)), (b) Six-channel mouse bone tissue (left
leg femur, infected treated unsuccessfully) (S.aureus: visual-
ized in blue, RUNX2: visualized in pink, nuclei: visualized in
green, actin cytoskeleton: visualized in red, collagen (SHG 2
photon): visualized in orange), and (c) One-channel zebrafish
embryo mitosis and cytokinesis, Functional Enhanced Green
Fluorescent Protein (EGFP).
A. Microscope system
The microscope is an optical device that is utilized to
view an object and areas of objects that are not possible
to visualize with our naked eyes (cells). The image of a
sample is magnified through at least one individual lens in the
microscope. This lens bends light towards the eye and makes
a sample appear larger than it is [10]. In the present context,
several microscopes are available in the market, some are built
for performing research in the field of science (biological,
geological, material, etc.) and some are built for classrooms or
personal hobby use. Optical (or light) and electron microscopy
are the two most popular microscope modalities, which are
mostly utilized in the field of cellular organisms. These two
microscopes involve the refraction or reflection, diffraction of
electron beams / electromagnetic radiation interacting with the
samples, to obtain a signal to create an image or to obtain a
collection of scattered radiation. In this study, we use two
demanding modalities, the confocal laser scanner microscope
(CLSM) and the focused ion beam scanning electron mi-
croscopy (FIB-SEM).
1) Confocal
Scanner (CLSM): CLSM are the special
types of fluorescence microscope that use fluorescent samples
[11]. In CLSM, two pinholes apertures are positioned at
confocal positions, the laser beam or light is focused by the
initial pinhole on only a small section of the specimen. The
second pinhole placed in the focal plane selects only the light
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B. Digital Imaging and Communication in Medicine (DI-
this image was obtained from Helios nanolab
scanner. Figure 2 shows the sample of the FIB-SEM image.
data sets, each packed into a single file. DICOM files are
composed of a multitude of DICOM Data element, and each
Data element object carries meta-information related to the
image such as patient and clinical staff information, acquisition
equipment properties, radiation dose, and structured reports.
The file header contains a 128-byte File Preamble which
is accompanied by a 4-byte DICOM prefix. The image file
may not have the file header (though one should be
included to comply with DICOM Standard). Likewise, Data
Set symbolizes an instance of a real-world world information
object. Each image file shall contain an individual Data
set expressing a single SOP (Service-Object Pair) Instance
linked to an individual SOP Class (and corresponding to
IODs (Information Object Definitions)), but an image file may
include more than one 2D image frames as particular IODs
may be defined to comprise multiple frames. The Transfer
Syntax is utilized to encode the image Data set which shall
be the one specified by the Transfer Syntax DID class of
the DICOM standard File Meta Information. A Data set
signifies an example of a real-world Information Object. It
is constructed with Data Elements. Data elements include
encoded values of features of that sample. The specific matter
and semantics of these features are specified in IODs (see PS
3.3: Information Object Definitions of the DICOM Standard)
[9]. The construction, characteristics, and encoding process of
a Data set and structure of Data elements are described in PS
3.5: Data Structures and Encoding of the DICOM Standard.
Pixel Data, Curves, and Overlays are Data elements whose
interpretation are based on other related elements.
D. Proposed method
This section proposes and describes an automatic DICOM
conversion pipeline which can intake different proprietary file
formats of CLSM and FIB-SEM modalities as input and,
after following some internal pipeline stages, later at the final
stage, we will receive an image into the DICOM standard
format where biological information related to the image is
stored inside metadata. Figure 3 shows the proposed schematic
diagram for the conversion of proprietary formats of CLSM
and FIB-SEM images into DICOM standard. The proposed
pipeline is divided into five parts. In the first step, we have
to pass an input image (both modalities can be passed with
C. Dicoogle
Dicoogle is an open-source DICOM PACS archive system
(https://dicoogle.coml) whose modular architecture allows the
quick development of new features, due to the accessibility of
a software development kit (SDK). It was designed with the
characteristics of automatic extraction, storage and indexing
of all metadata associated with the image images, including
private DICOM tag attributes, without re-engineering or re-
configuration requirements [13]. Besides, supporting standard
DICOM semantics, Dicoogle leverages diverse intranet and
web-based communication models and as well as document-
based indexing methods within a DICOM network, differing
from the common architecture utilizing relational databases.
(d). Dicoogle
(b). File
ICreate a file header and start I
to fill public tags
ICreation of private tags I
based on the supplied image
ISelection of unique I
SOPInstance UID
(c). Dicom
(e). Visualization
Fig. 3: Proposed schematic pipeline is divided into five parts:
(a) Input image, (b) File reader, (c) DICOM converter, (d)
Dicoogle PACS service, and (e) Visualization.
DICOM is a standard that medical institutions use to share
medical data between different systems made by different
vendors. It has been widely embraced by hospitals and medical
institutes and is making inroads in smaller applications to
doctors offices. It also enables the integration of acquisition
equipment, workstations, printers, servers, and network hard-
ware from several manufactures into PACS. DICOM is an
internationally approved standard for all medical imaging ac-
quisition modalities. The standard was introduced in response
to the advancement of the computer-aided imaging system in
the 1970s by a mutual group from the National Electrical
Manufacturers Association (NEMA) and the American Col-
lege of Radiology (ACR). They first published an ACR-NEMA
international standard in 1985 and later updated it in 1988 to
address the problem of vendor-independent data file formats
and data transmissions for digital medical images [9]. Version
3, which saw the name changed to DICOM, was published in
1993. The standard illustrates how to format and swap medical
image modalities and associated information, together within
the hospital and as well outside the hospital (e.g., telemedicine,
teleradiology). The standard has been held up to date with
the release of supplements that support the compatibility
with newer acquisition equipment and technologies. DICOM
serves as a standard for the transferal of radiologic image
modalities and other medical bioimaging information between
computers, allowing digital communication between systems
from various manufacturers and across different platforms
(e.g., Linux, Apple, or Windows). Medical image formatted
under the DICOM standard are binary files composed of
logical sections: a metadata header and image, i.e., pixel array
data. A DICOM image file consists of a file header and image
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Fig. 4: Converted
fluorescent microscope image into
standard format (a). Three-channel human islet mi-
crovasculature image converted into
file (channel
1: glucagon, channel 2: smooth muscle actin, and channel
3: collagen IV), (b). Six-channel mouse
tissue image
converted into
file (left leg femur, infected treated
S.aureus, channel 2: RUNX2,
channel 3: nuclei, channel 4: actin cytoskeleton, channel 5:
white light information, channel 6: collagen (SHG 2photon),
and (c) One-channel zebrafish embryo mitosis and cytokinesis,
Functional Enhanced Green Fluorescent Protein (EGFP).
channel colour intensity ranges from (0-255).
Channel 6
Channel 3
Channel 5
Channel 2
Channel 0
Channel 4
separate the composite image based on the number
into a single channel, and then we pass every single channel
(which consists of several slices) one by one to the
conversion pipeline for the conversion. The converted
images into
standard are shown in Figure 4 (a), (b),
and (c). Moreover,
modality images were already in
aslice and have an extension
.TIF, therefore, we
pass each slice through the pipeline which we have described
above for the conversion
files. Likewise,
Figure 5 (a) shows the converted
image into
and figure 5 (b) shows the metadata (where bioimaging infor-
mation are stored) related to Figure 5 (a), metadata consists
information like rows, column, pixel data, SOPClassUID,
modality, photometric interpretation, bits allocated, Transfer
Syntax UID, etc., tags
sample. The proposed
their z-stacklwithout z-stack or time-Iaspe/without time-lapse),
in the second step, a file reader module uses an open-source
OME-Bioformats library to read and parse the input image
based on the input file extension. The library was utilized
in a python environment to decode
microscopy images format. Although, OME-Bioformats code
is written in java, but with the help of Java-bridge, we can use
their library in a python environment. In the third stage, the
conversion function will get activated, making use
the open-source Pydicom 2.0.0 library. This library processes
very fast to read and write
files in a python envi-
ronment. It will create a new dataset main object files which
will wrap the dictionary files like
UID, Implementation Class UID, Transfer Syntax UID, etc.,
into file
information. The 1.2.840.10008.
SOPClassUID (unique identifier) was selected for the
microscope image because this SOPClassUID is
created specifically for visual microscopic image storage.
Photometric interpretation attributes, the "MONOCHROME2"
option was selected, as the provided sample was grayscale
with a sample
pixel one. Moreover, some private tags were
also created to include channel name, channel ID, microscope
name, channel filter name, magnification range, etc., because
file header, by the
committee, does not provide public tags to insert this kind
of information inside the metadata. Likewise, the SHA-1 hash
function library was applied to generate aunique patient ID
for each and every sample.
pipeline can convert both 8-
bit and 16-bit imaging modalities into
standard with
their bits stored tags. In the fourth pipeline stage, the Dicoogle
PACS service application was used to indexed and store the
images in the server. In the final stage,
the dicomized image can be viewed in any open-source or
commercially available
In this part, we will provide the results obtained after
executing the aforementioned process for converting several
proprietary file formats
modalities into
standard format. For this experiment,
we have supplied four input samples (which were obtained
from the tissues of mouse, human beings, zebrafish, and
healthy mouse liver tissue), from which three belong to the
microscope and the remaining one belongs to the FIB-
electron microscope.
samples were in a composite form, which means
that their channels were stacked with each other, and each
channel had a number
several slices that were attached
with each other (or were az-stack or time-lapse image),
as shown in Figure 1, and they have an extension of.CZI
and.LSM and belong to Zeiss vendor. For example, Figure
is a three-channel image with seventy-three slices; similarly,
Figure 1(b) is a six-channel image with twenty-eight slices
stacked with each other; and Figure 1(c) is a one-channel
image with ten z-stack slices and each z-stack includes twenty-
nine time-lapse images. Therefore, at the very first step, we
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_ Image
. 2 3 4 5 > »
oStudy• Patient
[ F<€etexto" dvanced ouecv
Fig. 6: Dicoogle PACS service (a). Shows the list of DICOM
images indexed into Dicoogle PACS server, and (b). Shows
the slices of FIB-SEM electron microscopy images converted
into DICOM format with metadata.
one belongs to CLSM: .LSM modality, and one belong to FIB-
SEM: .TIF modality) into DICOM standard, now the images
can be visualized in any DICOM viewer. Dicoogle provides
DICOM network interfaces and the images were successfully
consumed by third-party visualization software, demonstrating
this full interoperability of the proposed system. Now, these
data can be shared to any research or medical institute, can
be viewed at a once at a same or different network, and can
also be accessed more than researcher at a once. Furthermore,
the data can be managed and processes with tools available
in the common medical imaging viewers. Figure 7(a) and (b)
shows the 3D rendering view of human islet microvascular
of CLSM sample and healthy mouse liver tissue of FIB-
SEM sample. The 3D visualization rendering is based on
the maximum intensity projection algorithm, which is a very
common algorithm to render volume data. Furthermore, as
shown in Figure 7 (a), the three-channel CLSM sample (each
channel is represented by a distinct color) is stacked witheach
other when visualized in three dimensions. By visualizing this
image in 3D, has increased the insight research which was not
possible while visualizing it in 2D form.
0002,0000 ---: 176
0002,0002 Media Storage SOP Class UID: 1.2.840.10008.
0002,0003 Media Storage SOP Inst UID: 1.2.840.10008.
0002,0010 Transfer Syntax UID: 1.2.840.10008.1.2
0002,0012 Implementation Class UID: 1.2.826.0.1.3680043.8.498.1
0002,0013 Implementation Version Name: PYDICOM 2.1.2
0008,0016 SOP Class UID: 1.2.840.10008.
0008,0018 SOP Instance UID: 1.2.840.10008.
0008,0020 Study Date: 20210530
0008,0030 Study Time: 175535.607790
0008,0060 Modality: FIB-SEM
0008,1030 Study Description: Healthy_mouseJiver_tissue (left_leg_femur)
0010,0010 Patient's Name: Healthy_mouseJiver_tissue
0010,0020 Patient ID: 474578267179048
0010,0040 Patient's Sex: M
0020,000D Study Instance UID:
0020,000E Series Instance UID:
0020,0011 Series Number: 1
0020,0013 Image Number: 330
0020,1002 Images in Acquisition: 1
0020,4000 Image Comments: FIB-SEM
0028,0002 Samples per Pixel: 1
0028,0004 Photometric Interpretation: MONOCHROME2
0028,0006 Planar Configuration: 1
0028,0008 Number of Frames: 1
0028,0010 Rows: 2143
0028,0011 Columns: 2769
0028,0100 Bits Allocated: 8
0028,0101 Bits Stored: 8
0028,0102 High Bit: 7
0028,0103 Pixel Representation: 0
7FEO.0010 Pixel Data: 712
method has performed very well while converting the input
samples into a DICOM format. After conversion, the converted
image were sent to the Dicoogle PACS server for indexing and
to store over there, so that we can manage, share or view the
slices or download it from any part of the world. Figure 6(a)
shows the list of the microscopy samples that arrived to the
Dicoogle PACS server for indexing. Figure 6(b) shows the
dicomized slices of the FIB-SEM sample.
It should be noted that, after conversion of passed four
proprietary file formats (two belong to CLSM: .CZI modality,
Fig. 5: (a). Shows the converted FIB-SEM electron microscopy
image of healthy mouse liver tissue into DICOM standard
format, and (b). Shows the metadata of the same DICOM
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Fig. 7: 3D rendering utilizing maximum intensity projection
algorithm (a). Showing 3D view of three-channel human islet
microvasculature of CLSM sample which are converted into
DICOM (each channel is represented by different color) and
(b) showing 3D view of Healthy mouse liver tissue of FIB-
SEM sample which was converted into DICOM.
In this study, we created an automated pipeline that can
accept any proprietary CLSM and FIB-SEM file formats and
convert them to DICOM standard format with metadata. To
test the utility of the proposed approach, we ran it through
four proprietary files (three from CLSM and one from FIB-
SEM), and the results are shown in Figures 4 and 5. We also
sent these converted images to the Dicoogle PACS service for
indexing and storage, and we later viewed them in a DICOM
In the future, we intend to add more proprietary file formats
into our pipeline, as well as an annotation tool for segmenting
each region of interest so that classification may be applied to
This work was supported by the Marie Sklodowska Curie
ITN-EID, Horizon 2020 project IMAGE-IN (grant agreement
No 861122). We thank Christina Ebert (Center for Sepsis
Control and Care, CSCC), Astrid Tannert (Leibniz-IPHT), and
Ute Neugebauer (CSCC) for providing CLSM fluorescence
image and as well as FIB-SEM electron microscopy image.
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... Finally, in [64], a framework for the integration of multiple microscopy image modalities into the PACS-DICOM universe, including numerous metadata elements, was proposed. In [64], the authors developed a proof-of-concept system for validation purposes that, integrated with the open source PACS Dicoogle, provides image storage, metadata indexing, and visualization. ...
... Finally, in [64], a framework for the integration of multiple microscopy image modalities into the PACS-DICOM universe, including numerous metadata elements, was proposed. In [64], the authors developed a proof-of-concept system for validation purposes that, integrated with the open source PACS Dicoogle, provides image storage, metadata indexing, and visualization. ...
DICOM (Digital Imaging and Communication in Medicine) is a standard for image and data transmission in medical purpose hardware and is commonly used for viewing, storing, printing and transmitting images. As a part of the way that DICOM transmits files, the PACS (Picture Archiving and Communication System) platform, Dicoogle, has become one of the most in-demand image processing and viewing platforms. However, the Dicoogle PACS architecture does not guarantee image information recovery in the case of information loss. Therefore, this paper proposes a file recovery solution in the Dicoogle architecture. The proposal consists of maximizing the encoding and decoding performance of medical images through computational parallelism. To validate the proposal, the Java programming language based on the Reed-Solomon algorithm is implemented in different performance tests. The experimental results show that the proposal is optimal in terms of image processing time for the Dicoogle PACS storage system.
... Likewise, the importance of cell imaging in pathogen niche research has grown significantly and this includes investigations on living cells such as cell phase identification, cell tracking, and tracking of subcellular components [5][6][7]. 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 gath-ered 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]. ...
... 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. ...
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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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Background Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.
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Light microscopy is a key tool in modern cell biology. Light microscopy has several features that make it ideally suited for imaging biology in living cells: the resolution is well-matched to the sizes of subcellular structures, a diverse range of available fluorescent probes makes it possible to mark proteins, organelles, and other structures for imaging, and the relatively nonperturbing nature of light means that living cells can be imaged for long periods of time to follow their dynamics. Here I provide a brief introduction to using light microscopy in cell biology, with particular emphasis on factors to be considered when starting microscopy experiments.
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Image file format is often a confusing aspect for someone wishing to process medical images. This article presents a demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (Dicom). Concepts common to all file formats, such as pixel depth, photometric interpretation, metadata, and pixel data, are first presented. Then, the characteristics and strengths of the various formats are discussed. The review concludes with some predictive considerations about the future trends in medical image file formats.
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Content-based image retrieval (CBIR) has been heralded as a mechanism to cope with the increasingly larger volumes of information present in medical imaging repositories. However, generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce. In this article we propose a methodology for parametric CBIR based on similarity profiles. The architecture and implementation of a profiled CBIR system, based on query by example, atop Dicoogle, an open-source, full-fletched PACS is also presented and discussed. In this solution, CBIR profiles allow the specification of both a distance function to be applied and the feature set that must be present for that function to operate. The presented framework provides the basis for a CBIR expansion mechanism and the solution developed integrates with DICOM based PACS networks where it provides CBIR functionality in a seamless manner.
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Advances in imaging techniques and high-throughput technologies are providing scientists with unprecedented possibilities to visualize internal structures of cells, organs and organisms and to collect systematic image data characterizing genes and proteins on a large scale. To make the best use of these increasingly complex and large image data resources, the scientific community must be provided with methods to query, analyze and crosslink these resources to give an intuitive visual representation of the data. This review gives an overview of existing methods and tools for this purpose and highlights some of their limitations and challenges.
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The Digital Imaging and Communications in Medicine (DICOM) Standard specifies a non-proprietary data interchange protocol, digital image format, and file structure for biomedical images and image-related information. The fundamental concepts of the DICOM message protocol, services, and information objects are reviewed as background for a detailed discussion of the functionality of DICOM; the innovations and limitations of the Standard; and the impact of various DICOM features on information system users. DICOM addresses five general application areas: (1) network image management, (2) network image interpretation management, (3) network print management, (4) imaging procedure management, (5) off-line storage media management. DICOM is a complete specification of the elements required to achieve a practical level of automatic interoperability between biomedical imaging computer systems--from application layer to bit-stream encoding. The Standard is being extended and expanded in modular fashion to support new applications and incorporate new technology. An interface to other Information Systems provides for shared management of patient, procedure, and results information related to images. A Conformance Statement template enables a knowledgeable user to determine if interoperability between two implementations is possible. Knowledge of DICOM's benefits and realistic understanding of its limitations enable one to use the Standard effectively as the basis for a long term implementation strategy for image management and communications systems.
Carl Zeiss, Ernst Abbe, and Advances in the Light Microscope - Volume 25 Issue 4 - Wolfgang Wimmer
The Open Microscopy Environment (OME) has built and released Bio-Formats, a Java-based proprietary file format conversion tool and OMERO, an enterprise data management platform under open source licenses. In this report, we describe new versions of Bio-Formats and OMERO that are specifically designed to support large, multi-gigabyte or terabyte scale datasets that are routinely collected across most domains of biological and biomedical research. Bio-Formats reads image data directly from native proprietary formats, bypassing the need for conversion into a standard format. It implements the concept of a file set, a container that defines the contents of multi-dimensional data comprised of many files. OMERO uses Bio-Formats to read files natively, and provides a flexible access mechanism that supports several different storage and access strategies. These new capabilities of OMERO and Bio-Formats make them especially useful for use in imaging applications like digital pathology, high content screening and light sheet microscopy that create routinely large datasets that must be managed and analyzed.
The last decade has seen a rapid increase in the number of tools to acquire volume electron microscopy (EM) data. Several new scanning EM (SEM) imaging methods have emerged, and classical transmission EM (TEM) methods are being scaled up and automated. Here we summarize the new methods for acquiring large EM volumes, and discuss the tradeoffs in terms of resolution, acquisition speed, and reliability. We then assess each method's applicability to the problem of reconstructing anatomical connectivity between neurons, considering both the current capabilities and future prospects of the method. Finally, we argue that neuronal 'wiring diagrams' are likely necessary, but not sufficient, to understand the operation of most neuronal circuits: volume EM imaging will likely find its best application in combination with other methods in neuroscience, such as molecular biology, optogenetics, and physiology.