L. Rodney Long, National Library of Medicine, USA
Sameer Antani, National Library of Medicine, USA
Thomas M. Deserno, Department of Medical Informatics, Germany
George R. Thoma, National Library of Medicine, USA
Content-based image retrieval (CBIR) technology has been proposed to benefit not only the management of increasingly large image collections, but also to aid clinical care, biomedical research, and education. Based on a literature review, we conclude that there is widespread enthusiasm for CBIR in the engineering research community, but the application of this technology to solve practical medical problems is a goal yet to be realized. Furthermore, we highlight "gaps" between desired CBIR system functionality and what has been achieved to date, present for illustration a comparative analysis of four state-of-the-art CBIR implementations using the gap approach, and suggest that high-priority gaps to be overcome lie in CBIR interfaces and functionality that better serve the clinical and biomedical research communities.
Keywords: content-based image retrieval (CBIR); feature extraction; image indexing; medical image retrieval; performance; usability; Web user interface
Content-based image retrieval (CBIR) technology exploits the visual content in image data. It has been proposed to benefit the management of increasingly large biomedical image collections as well as to aid clinical medicine, research, and education [1-2]. We treat CBIR as a set of methods that (1) index images based on the characteristics of their visual content, and (2) retrieve images by similarity to such characteristics, as expressed in queries submitted to the CBIR system. These characteristics, also referred to as "signature", may include intensity, color, texture, shape, size, location, or a combination of these. Sketching a cartoon, selecting an example image, or a combination of both methods, is typically used to form the query. The retrieved results are usually rank-ordered by some criteria; however, other methods, such as clustering of similar images, have been used to organize the results as well.
Practical application of CBIR depends on many different techniques and technologies applied at several stages in the indexing and retrieval workflow, such as: image segmentation and feature extraction; feature indexing and database methods; image similarity computation methods; pattern recognition and machine learning methods; image compression and networking for image storage and transmission; Internet technologies (such as JavaScript, PHP, AJAX, Applet/Servlet); and human factors as well as usability. More recently, natural language processing has also been included, in attempts to exploit text descriptions of image content and the availability of standardized vocabularies [3]. It is through careful selection of appropriate methods from these fields that a successful CBIR application can be developed.
The technical literature regularly reports on experimental implementations of CBIR algorithms and prototype systems, yet the application of CBIR technology for either biomedical research or routine clinical use appears to be very limited. While there is widespread enthusiasm for CBIR in the engineering research community, the incorporation of this technology to solve practical medical problems is a goal yet to be realized. Possible obstacles to the use of CBIR in medicine include:
Therefore, we take these four factors: content, features, performance, and usability as foundational in classifying and comparing CBIR systems, and in this paper we use these concepts as (1) an organizational principle for understanding the "gaps", or what is lacking in medical CBIR systems, (2) a lens for interpreting the main trends and themes in CBIR research over the past several years, and (3) a template for a systematic comparison of four existing biomedical CBIR systems.
The concept of gaps has often been used in CBIR literature, with the semantic gap being the most prominent example [1,2]. We have treated this "concept of gaps" as a paradigm for a broad understanding of what is lacking in CBIR systems and have extended the gap idea to apply to other aspects of CBIR systems [4], beyond the semantic gap. We may consider the semantic gap to be a break or discontinuity in the aspect of image understanding, with "human understanding" on one side of the gap and "machine understanding" on the other. Similarly, we may identify breaks or discontinuities in other aspects of CBIR systems, including the level of automation of feature extraction, with full automation on one side and completely manual extraction on the other; and, for another example, the degree to which the system helps the user refine and improve query results, with "intelligent" query refinement algorithms based on user identification of "good" and "bad" results on one side, and no refinement capability at all on the other. Each gap (1) corresponds to an aspect of a CBIR system that is explicitly or implicitly addressed during implementation; (2) divides that aspect between what is potentially a fuller or more powerful implementation from a less powerful one; and (3) has associated with it methods to bridge or reduce the gap.
In order to assess medical CBIR retrospectively, we searched the web for relevant articles and identify the focus fields of past and current research. Using the concept of gaps [4], we also present the relevant differences in current medical CBIR systems illustratively, based on four state-of-the-art medical CBIR systems. Based on this analysis, we try to reliably predict future directions of medical CBIR, which we believe to be most important.
As a measure of types of research activity in the field of medical image retrieval, and of relative importance given to addressing particular system gaps, we surveyed the references to terms commonly used in the context of medical image retrieval in ten journals over the years 2001-2007. The journals were identified using informal selection criteria, but with the goal of providing a broad representation of the major publications reporting medical image retrieval research results. The journals and publishers are listed in Table 1. We followed a methodology similar to that discussed by Datta et al. [5], who carried out similar work for general image retrieval. Using Google Scholar (http://scholar. google.com), we searched for the terms {"medical image retrieval" AND search_phrase}, where search_phrase was one of the CBIRrelated phrases given in Table 2.
Table 1. Journals surveyed for medical image retrieval terms
Table 2. Search phrases ANDed with "medical image retrieval", in decreasing order of number of citations. Abbreviations are used in Figure 1.
In [4], we have identified a total of 14 gaps, and organized them into the basic "gap categories" given above: "Content Gaps," "Feature Gaps," "Performance Gaps," and "Usability Gaps." In addition to the gaps, other characteristics are useful to specify and distinguish medical CBIR systems. In [4], we group these under the general category of "system characteristics", which we further categorize as follows: (1) "intent and data" (the goal of using CBIR in the particular system, and the data that is used with it); (2) "input and output" (the specific I/O content); and (3) "feature and similarity" (the kind of features and distance measures used by the system).
The use of the concept of gaps, supplemented by system characteristics, has been proposed as a general methodology for comparative evaluation of CBIR systems, and for design planning in creating new systems. This conceptual organization is an effort towards encapsulating in a structured fashion the lessons learned in the published CBIR literature, and making system comparisons more comprehensive and practical.
In this paper, we illustrate the concrete application of these concepts to four state-ofthe- art medical CBIR systems that are available online to the public via the Internet. All systems have been developed by at least one of the authors of this paper. This selection avoids problems that are generally associated with the judgment of work of other researchers.
Based on the retrospective literature review and comparative system overview, we suggest high-priority areas critical for moving CBIR into practical medical use. By its nature, this part is rather subjective and represents the personal viewpoints of the authors, rather than objective facts.
The early years of medical CBIR have been reviewed by Müller et al [2]. As described in Section 2.1, we focus on the years 2001 through 2007. The number of citations returned for each of the search phrases is presented graphically in Figure 1.
Inspection of Figure 1 shows, first of all, a high number of citations for the phrase "Content- Based Image Retrieval", which supports the idea that much of the medical image retrieval work in the engineering research community over the period investigated has in fact been related to CBIR. Other phrases near the high end of the citation scale suggest that most research attention has been in the areas of indexing, statistical methods, and learning methods. In terms of gaps addressed, the survey tends to support the view that most of the CBIR research effort over the surveyed years has been in addressing the "Feature Gap Category", that is, the set of gaps dealing with the extraction of mathematical features from the images.
Figure 1. Journal citation results (for phrases related to medical image retrieval) for journals surveyed 2001-2007. For list of journals, see Table 1. For explanation of abbreviations on xaxis, see Table 2.
At the lower end of the citation scale were the phrases referring to user interface, performance, interactivity, and relevance feedback. We note that while there were a relatively large number of references to "Web" in the journals, the considerably lower numbers of references to "user interface" suggest that many of the Web references did not refer to actual Web user interfaces, but more likely general acknowledgments of the significance of the Web. In terms of gaps not addressed, or weakly addressed, it appears that only a relatively small fraction of the CBIR research effort has been directed to addressing the "Performance Gap Category" and the "Usability Gap Category".
In this section, we provide a concrete application of system analysis by gaps and system characteristics to four medical CBIR systems.
System Intent
The CervigramFinder system [6] operates on
cervicographic images (also called cervigrams)
and was created by the collaborative efforts of
the National Cancer Institute (NCI) and the
National Library of Medicine (NLM) for the
study of uterine cervix cancer. This cancer is
closely related to the chronic infection of certain
types of Human Papillomavirus (HPV).
To visually screen for pre-invasive cervical
lesions or for cancer, one cost-effective method
is cervicography. Cervicographic screening
is based on the acetowhitening phenomenon:
HPV-infected abnormal tissue often turns white
after being treated with 3-5% acetic acid. A
cervigram is a 35-mm photograph of the cervix
taken approximately one minute after acetic
acid exposure. NLM has created a cervigram
database containing approximately 100,000
cervigrams taken during two major projects in
cervical cancer carried out by NCI to study the
natural history of HPV infection and cervical
neoplasia, the Guanacaste and ALTS projects
[7-8]. In addition to cervigrams, correlated
clinical, cytologic and molecular information
were also collected.
Interface
CervigramFinder operates on a subset of the
cervigram database. To use this system, the
user defines a query region by marking a region
of interest on an image through the graphical
user interface shown in Figures 2a and 2b. (In
the query shown in these Figures, the user is
searching on the "location" feature and is limiting
the search to regions that already have the
semantic labeling "AW", for "acetowhitened".)
The system then (1) calculates the feature vector
of the query region for the specified features and
(2) compares that query feature vector with the
pre-computed feature vectors of regions stored
in the database. The returned regions, shown on
their parent images, are ranked by the degree
of their similarity to the query feature vector
and presented on a multi-image display, along
with associated text information. The (visual)
features used are color, texture and size. Shape
is significantly less important as a feature for
identifying or distinguishing regions in this application
since these tissue types do not exhibit
any particular shape except for the os regions
(the os is the opening into the uterus) which
are somewhat elliptic. In order to facilitate
system evaluation by medical experts located
at geographically different sites, as well as to
allow the final system to be accessed remotely
for either diagnosis or education in the future,
the system is implemented using a distributed
client/server framework.
Gaps and System Characteristics
Gap and system characteristics of Cervigram-
Finder are given in Tables 3 and 4, respectively,
which provide a side-by-side comparison with
corresponding gaps and characteristics of the
CervigramFinder, SPIRS, IRMA, and SPIRSIRMA
systems. Significant gaps that are yet to
be addressed in the CervigramFinder system
include, for Feature Gaps, lack of multiscale
analysis (only single-scale is used) ; for Performance
Gaps, lack of integration into use in a
biomedical system and lack of database indexing;
for Usability Gaps, neither user feedback
on relative similarity of returned images, and
nor query refinement is provided. Capabilities
that have at least partially addressed some gaps
include, for Content Gaps, semantic labeling of
regions in the database images; for Feature Gaps,
some computer-assisted feature extraction (for
indexing features, a user must manually mark
boundaries of significant regions; algorithms
then compute mathematical features from
these regions); for Performance Gaps, online
implementation and qualitative retrieval evaluation;
and, for Usability Gaps, retrieval by both
user selection of pre-stored regions-of-interest
("query by composition") and by interactive user
sketch. We also note that CervigramFinder has
been exercised by several medical experts with
their system interactions digitally recorded, for
improvement of usability of the system. The
system characteristics of CervigramFinder
indicate that it is for research, teaching, and
learning; it uses 2D data; it operates only on
image data, both for input and output. We note
also that CervigramFinder operates on color
image data, making it unique in that respect
among the four systems that we discuss.
Figure 2a. CervigramFinder interface; "feature" panel in lower left shows that user is searching on "location"
Figure 2b. CervigramFinder interface "region" panel, showing that user may limit search to semantically-labeled region types
System Intent
The Spine Pathology & Image Retrieval System
(SPIRS) [9] was developed at the U. S. National
Library of Medicine to retrieve x-ray images
from a large dataset of 17,000 digitized radiographs
of the spine and associated text records.
Users can search these images by providing a
sketch of the vertebral outline or selecting an
example vertebral image and some relevant
text parameters. Pertinent pathology on the
image/sketch can be annotated and weighted
to indicate importance. This hybrid text-image
query yields images containing similar vertebrae
along with relevant fields from associated text
records, which allows users to examine the
pathologies of vertebral abnormalities.
Interface
SPIRS provides a Web-based interface for image
retrieval using the morphological shape of
the vertebral body. A query editor enables users
to pose queries either by sketching a unique
shape, or by selecting or modifying an existing
shape from the database. Additional text fields
enable users to supplement visual queries with
other relevant data (e.g., anthropometric data,
quantitative imaging parameters, patient demographics).
These hybrid text-image queries
may be annotated with pertinent pathologies
by selecting and weighting local features to
indicate importance. Query results appear in
a customizable window that displays the top
matching results and related patient data. The
SPIRS interface is shown in Figure 3.
Gaps and System Characteristics
Significant gaps that are yet to be addressed
in the SPIRS system are similar to those for
CervigramFinder, and include, for Feature
Gaps, lack of multiscale analysis; for Performance
Gaps, lack of integration into use in a
biomedical system and lack of quantitative
evaluation; for Usability Gaps, no user query
refinement. (However, see comments about
"data exploration" below.) Capabilities that
have at least partially addressed some gaps
include, for Content Gaps, manual labeling
of vertebrae by anatomical type; for Feature
Gaps, computer-assisted feature extraction (an
Active Contours algorithm is used to find approximate
boundaries of vertebrae in the images;
these boundaries then are manually reviewed
and corrected); for Performance Gaps, feature
vector indexing by K-D Tree, and qualitative
evaluation; and, for Usability Gaps, support
for both query by composition (see Section
2.1.1) and by interactive user sketch. We also
note that SPIRS provides capability to specify
not only the shape to be used in the query, but
which part of the shape should be used, so that
the user may focus on the fine level of structure
that is often critical in biomedical image
interpretation. In addition, SPIRS provides (1)
"basic" user feedback on each returned image,
namely, a figure of dissimilarity to the query
image; and (2) a "data exploration" capability,
which takes query results as a beginning
point to initiate new and related queries; using
a given query result, that is, a vertebral shape
returned by a query, the entire spine containing
that shape may be displayed; then the user may
select a vertebra in that same spine and use its
shape as a new query. It should be noted that
SPIRS, like CervigramFinder, operates on local,
region-of-interest data in the image. The
system characteristics of SPIRS indicate that
it is for research, teaching, and learning on 2D
data; it accepts as input, and creates output
"hybrid"” data (both text and image). In this
regard, SPIRS allows the user to specify as a
query a vertebral shape and some text (such as
age, race, gender, presence/absence of back or
neck pain, and vertebra tags such as "C5", to
indicate the class of vertebrae being searched
for). It then returns such text, along with the
associated image data.
System Intent
The Image Retrieval in Medical Applications
(IRMA) project [10-12] has the following goals:
(1) automated classification of radiographs
based on global features with respect to imaging
modality, body orientation with respect to
the x-ray beam (e.g., "anterior-posterior" or
"sagittal"), anatomical body region examined,
and the biological system under investigation;
and (2) identification of local image features
including their constellation within a scene,
which are relevant for medical diagnosis.
These local features are derived from a priori
classified and registered images that have been
segmented automatically into a multi-scale
approach. IRMA analyzes content of medical
images using a six-layer information model:
(1) raw data, (2) registered data, (3) feature, (4)
scheme, (5) object, and (6) knowledge.
The IRMA system that is currently available via the Internet retrieves images similar to a query image with respect to a selected set of features. These features can, for example, be based on the visual similarity of certain image structures. Currently, the image data consists of radiographs. It uses a reference database of 10,000 images categorized by image modality, orientation, body region, and biological system.
Figure 3. SPIRS interface; example query for records satisfying criteria {(age >=60, gender=female, race=black) AND having vertebrae similar to lower/front of sketch)
Interface
The system architecture has three main components:
(1) the central database, containing
images, processing schemes, features, and
administrative information about the IRMA
workstation cluster; (2) the scheduler, which
balances the computational workload across
the cluster; and 3) the Web server, which provides
the graphical user interface to the IRMA
system for data entry and retrieval. Extended
query refinement is established by logging all
user interaction in the system database that also
hold the features extracted from the images
[12]. The IRMA system interface is shown in
Figure 4.
Gaps and System Characteristics
In contrast to the rather general concept within
the IRMA project, the IRMA system that is
currently demonstrated on the web has some
significant gaps that are still yet to be addressed.
These gaps include, for Content Gaps, lack
of semantic labeling; for Feature Gaps, only
operation on global image characteristics is
supported, and multiscale analysis is lacking; for
Performance Gaps, lack of integration into use
in a biomedical system, lack of feature vector
indexing, and lack of quantitative evaluation.
Capabilities that at least partially address system
gaps include, for Feature Gaps, fully automatic
feature extraction (facilitated, of course, by
the fact that IRMA operates on the image as
a whole, so that segmentation of particular
regions-of-interest prior to feature extraction
is not required); for Performance Gaps, a
widely-publicized and mature online Internet
presence, and qualitative retrieval evaluation;
and, for Usability Gaps, an extremely flexible
query refinement mechanism that lets the user
step back and forth among queries done in a
session, and lets the user combine queries with
union, intersection, and negation operators. This
is coupled with an advanced feedback measure
that assists the user in judging how closely a
retrieved image matches not only a single image
used in the query, but how closely it matches a
weighted set of images. The system characteristics
of IRMA indicate that it is for research,
teaching, and learning use on 2D data.
Figure 4a. IRMA query interface with relevance feedback. The initial query image was useruploaded from the user’s computer.
Figure 4b. The IRMA session logging provides complete access to previous session states.
System Intent
IRMA, described above, aims at providing
visually rich image management through CBIR
techniques applied to medical images using
intensity distribution and texture measures
taken globally over the entire image. This
approach permits queries on a heterogeneous
image collection and helps identify images that
are similar with respect to global features, e.g.,
all chest x-rays in the AP (anterior-posterior)
view. However, the IRMA system lacks the
ability to find particular pathology that may be
localized in specific regions within the image.
In contrast, the SPIRS system provides localized
vertebral shape-based CBIR methods for
pathologically sensitive retrieval of digitized
spine x-rays and associated metadata. In the
SPIRS system, the images in the collection
must be homogeneous, i.e., a single modality
imaging the same anatomy in the same view,
e.g., vertebral pathology expressed in spine
x-ray images in the sagittal plane. Observing
the different strengths of the two systems led
to the idea of combining these complementary
technologies to create an SPIRS-IRMA system
[13] that will eventually support both whole
image and local feature-based retrieval so that
users may find images that are not only similar in
overall appearance but also similar with respect
to locally-expressed pathology.
Interface
Initial work toward creating such a system
has begun and some capabilities are in place;
the current SPIRS-IRMA interface is shown
in Figure 5.
Gaps and System Characteristics
SPIRS-IRMA, then, is an example of combining
the capabilities of different CBIR implementations,
developed by different research groups,
as a strategy of closing CBIR gaps of the
individual systems. We noted above that the
IRMA system operates on global image data
only, while the SPIRS system operates only on
local region-of-interest image data that has been
segmented from the image. The SPIRS-IRMA
system is the first step toward a system that will
integrate the capabilities of these two systems.
At the current time, the SPIRS capabilities for
retrieval by vertebrae shape similarity, and the
SPIRS vertebrae shape database, have been
coupled to the IRMA user interface, so that an
IRMA user has full access to SPIRS for vertebrae
retrieval by shape. A user may log in to
the IRMA system and access an interface that
enables the retrieval of spine vertebrae by shape.
This capability uses the combined resources of
servers operating in Germany (Aachen) and the
U.S. (Bethesda, Maryland) which are linked
through an XML-based service protocol that is
used to coordinate the transmission of the query
and the query results between the servers.
This system lays the groundwork to perform global image searches to identify images of interest, and then to use local region-of-interest search capability to drill down into specific localized anatomy or pathology. It already combines the IRMA interface (with session query management), with the local region search capability of the SPIRS system.
While the goal is for the SPIRS-IRMA system to eventually possess all of the strengths of both systems, the current, initial system, provides only some of these capabilities. Also, some of the individual system strengths are not available in the current SPIRS-IRMA implementation (for example, SPIRS returns both images and keywords, but SPIRS-IRMA returns only images). Significant gaps yet to be addressed in the SPIRS-IRMA system include the following: no semantic content is available to the user (the manual semantic labeling of SPIRS is not yet available under SPIRS-IRMA), the image structure that may be used in queries is only local, as in SPIRS, at the current time, and only query by composition (pre-stored shapes) is available (SPIRS-IRMA does not allow interactive sketch). A gain over the SPIRS system, though, has been the narrowing of Performance and Usability gaps through the use of the well-known IRMA interface, and by the versatility of its session management capabilities available for searching the SPIRS data. The future joining of the two systems to create image search by both global and local characteristics will add capability that is rare if not unique in the medical CBIR field. The system characteristics of SPIRS-IRMA indicate that its use is for research, teaching, and learning use on 2D data.
Figure 5a. SPIRS-IRMA interface for searching vertebra shapes.
Figure 5b. A vertebra shape is represented by 36 landmark points, and the user can select a partial shape of interest.
Table 3. System Gaps Compared Across CBIR Systems. (See reference [4] for complete explanation of terms. A few of the most significant are given here.) Semantic/Manual: some semantic labeling of image content; Use Context/Narrow: one or small number of image modalities; Scale/Single: no multiscale processing; Query/Composition: pre-stored shapes or patterns are used; Query/Sketch: interactive user sketch; Feedback/Basic: only similarity or dissimilarity to single query image is provided; Feedback/Advanced: measure of match to weighted image set is provided; Refinement/Complete Combination: complete query history in session is maintained and queries may be combined.
Table 3. System Gaps Compared Across CBIR Systems. (See reference [4] for complete explanation of terms. A few of the most significant are given here.) Semantic/Manual: some semantic labeling of image content; Use Context/Narrow: one or small number of image modalities; Scale/Single: no multiscale processing; Query/Composition: pre-stored shapes or patterns are used; Query/Sketch: interactive user sketch; Feedback/Basic: only similarity or dissimilarity to single query image is provided; Feedback/Advanced: measure of match to weighted image set is provided; Refinement/Complete Combination: complete query history in session is maintained and queries may be combined.
Creating effective collaborations among different, geographically-separated CBIR engineering research groups, and collaborations among the engineering and medical communities to advance this field, will likely remain a challenge for the foreseeable future. Nevertheless, certain efforts within the engineering community are worth noting, including (1) the important Image- CLEF competition [3], which allows evaluation of algorithmic approaches of multiple research groups on a single image test set, (2) the convening of CBIR workshops at professional conferences, such as those held at MICCAI in 2007 [14] and SPIE Medical Imaging in 2008 [15], (3) the collection of segmentation data from medical experts, (4) the exposure of CBIR systems to medical experts, though in small scale efforts to date, and (5) collaborative work to combine and make different CBIR systems interact, typified by SPIRS-IRMA, to exploit the strengths of the individual systems.
Table 4.
Effectively representing medical content by low-level mathematical features is essentially grappling with the semantic gap, which may possibly remain a perennial problem. This does not mean, however, that tools for retrieval by image content may not be made increasingly effective. Easy-to-use relevance feedback mechanisms, such as those supported by the IRMA system, ameliorate this situation somewhat by allowing the user to quickly refine queries by identifying specific returned results as desirable or not desirable. Our literature search suggests that this entire domain of relevance feedback has been under-researched, and we anticipate considerable room for growth and improvement of existing techniques.
Evaluation of CBIR systems has been a particularly difficult issue, with precision and recall measures frequently being used, but with a "ground truth" which may reflect a high degree of variability in expert opinion. The crucial threshold for medical CBIR system evaluation remains, of course, not a quantitative mark defined in the engineering environment, but the degree of usefulness to the biomedical community in such systems becoming truly valuable aids in clinical and research problemsolving.
It is common for engineering groups engaged in CBIR development to express a desire for closer collaboration with the medical community. It is less common to propose solutions for bridging this collaboration gap. We suggest more proactive steps to expose CBIR tools to the medical community as an effort to help overcome this problem. This entails both (1) understanding the types of biomedical problems for which CBIR can potentially have a clinical or research impact, and (2) tailoring tool interfaces to operate in the "patient-centric" mode of the medical environment; with the appropriate balance of simplicity and power, as judged by the medical user; with labeling and terminology appropriate for the medical user; and with interface capabilities for importing and exporting information from other data sources that are important to the medical user.
Success of a particular technology is often due to the confluence of available, supporting technologies at the time of critical need. Content- Based Image Retrieval of medical images has achieved a degree of maturity, albeit at a research level, at a time of significant need. However, the field has yet to make noticeable inroads into mainstream clinical practice, medical research, or training. In this article we have explored the field through the concept of gaps or shortcomings in comparison with an idealized system. By addressing and minimizing these gaps, a system may be better positioned for use in the biomedical world. We have characterized CBIR system gaps under the broad categories of content, feature, performance, and usability and suggest that the published CBIR technical literature reflects too little attention to closing the gaps of performance and usability, although these are perhaps the gating factors that limit closer collaboration with the biomedical community. We suggest early, proactive system design incorporating the workflow, terminology, and modes of operation of the biomedical user as a needed effort for enhancing collaboration with the medical community.
This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM), and Lister Hill National Center for Biomedical Communications (LHNCBC).