- CerviScan
Ewert Bengtsson, Patrik Malm, Hyun-Ju Choi, Bo Nordin
Partners: Rajesh Kumar, CDAC, Centre for Development of Advanced Computing, Thiruvananthapuram, India and K Sujathan, Regional Cancer Centre, Thiruvananthapuram, India.
Funding: Swedish Governmental Agency for Innovation Systems (Vinnova) and Swedish Research Council
Period: 0801-
Abstract: Cervical cancer is killing a quarter million women every
year. Screening based on so called PAP-smears have proven very
effective to reduce cancer mortality but require much work of well
trained cytotechnologist. For 50 years research to automate the
screening has been in progress, Bengtsson was very active in this
field 1973-1993. Since about 10 years, commercial automated systems
have been in operation but unfortunately those systems have many
limitations. In India there is no effective screening program in
operation and around 70,000 women die from the disease each
year. Now an effort to develop a screening system adapted to Indian
situation is in progress at the research institute CDAC in
Thiruvananthapuram, Kerala in cooperation with the RCC, Regional
Cancer Centre there. Based on our earlier experience in the field we
are cooperating with this project and we have received support from
Vinnova and VR for this. At CDAC they have developed a program for
data collection which so far has been used at RCC to collect image
data from around 800 patients. They have also developed an overall
framework for the screening including image segmentation, artifact
rejection, feature extraction and cell and specimen
classification. They are currently evaluating the performance and
working on improving the various parts of the system as needed. We
are in particular studying whether the 3D chromatin texture of the
cervical cells can be utilized as a robust feature for detecting
(pre-) cancerous lesions. For that purpose we are scanning the cells
at 40 different focus levels creating stacks of data for each cell
nucleus. Dr Sujathan spent two weeks in Uppsala in October 2010 and
another two weeks in October 2011 to collect image stacks and
annotate cell nuclei with labels indicating the biological ``ground
truth'' to be used for training and testing our classifiers. Dr
Hyun-Ju Choi from Korea who was a post-doc at CBA 2008-2010 has
studied 3D nuclear texture for other applications and we are
currently evaluating her approaches for this purpose. In November
2011 we started collaboration with Andrew Mehnert at MedTech West,
Chalmers through which the texture approaches he developed in his
PhD thesis will be applied and evaluated. We have also implemented
several other feature extraction methods and adapted them for our
data stacks. Bengtsson spent the first week of 2011 in
Thiruvananthapuram discussing in detail how our approaches could be
integrated. These discussions continued in October 2011 when a
delegation from CDAC visited CBA.
- Tracking of Unstained Cells in Microfluidic Systems
Martin Simonsson, Carolina Wählby
Partners: Johen Kreuger, Sara Thorslund, Gradientech AB, Uppsala
Funding: SciLife Lab Uppsala
Period: 1108-
Abstract: Tracking of cell movements in various cell culture setups is
essential to many researchers in the life science sector. Gradientech
AB, a Swedish biotech company, has developed CellDirector, a unique
microfluidic system that academic researchers can use to study how
concentration gradients of soluble proteins impact cell migration. The
current project is focused on developing software for analyzing cell
behavior and cell migration. The free open-source software
CellProfiler developed at the Broad Institute will be used as a
platform for a high-throughput system with automated high quality
imaging, adapted for unlabeled cells, which are analyzed with regard
to directionality of migration, speed, and acceleration. Apart from
analyzing cell migration, the cell tracking aims at producing
lineages, where cellular events such as cell division and cell death
can be scored for single cells.
- Detection and Classification of Malaria Infected Cells by LED Specral Microscopy
Carolina Wählby
Partners: Jeremie Zoueu, Olivier Bagui, Dept. Genie Electrique et Electronique, Institut National Polytechnique, Felix Houbhouet-Boigny, Cote d' Ivoire
Period: 1109-
Abstract: This project aims to propose an effective optical device based on LED
spectral microscopy, which will be low cost, fast and easy to use in
the diagnosis of human malaria parasites, especially because the
sample will not need any special preparation or staining and the data
will be automatically processed to provide real-time diagnosis of the
type of the parasite, the parasitic density and its age for an
effective prescription. The collaborative project was initiated by a
3-month visit by Olivier Bagui, where we focused on the development of
efficient segmentation methods for unstained images of blood cells.
- Modelling Diffusion on Cellsurfaces
Ida-Maria Sintorn, Robin Strand
Partners: Ingela Parmryd, Dept. of Medical Cell Biology, UU; Jeremy Adler, Dept. Of Immunology, Genetics and Pathology, UU
Funding: TN-faculty UU; S-faculty, SLU; VINNMER programme, Swedish Governmental Agency for Innovation Systems
Period: 1101-
Abstract: A cell surface is a highly irregular and rough. The surface
topography is however usually ignored in current models of the
plasma membrane, which are based on 2D observations of diffusion
that really occurs in 3D. In this project we model diffusion on
non-flat surfaces to explain biological processes occurring on the
cellsurface.
- Endothelial Cell Segmentation of the Cornea of Human Eyes
Bettina Selig, Cris Luengo
Partners: Bernd Rieger, Quantitative Imaging Group, Delft University
of Technology, Netherlands; Koen Vermeer, Eye Hospital Rotterdam,
Netherlands
Funding: S-faculty, SLU
Period: 1103-
Abstract: In many corneal studies, endothelial cell density and morphology is
used to assess the quality of the cornea. Based on these parameters,
important therapeutic decisions are made. The endothelium may be
imaged by specular microscopy or by confocal scanners and measurements
can be obtained manually, automatically with manual corrections or
fully automatically with current software (e.g., Nidek's Navis).
Unfortunately, the results of the automatic mode are insufficient (see
Figure 8) when the image quality is affected or when irregular
shaped endothelial cells are present. In this project, we are
developing a new segmentation method, using stochastic watershed, that
enables a better estimation of endothelial quality.
Figure 8:
(a) Original image of endothelial cells
(b) Automatic segmentation with manually set seed points (by the
commercial software Nidek's Navis)
(c) Fully automatic segmentation (by the commercial software Nidek's Navis).
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- Spectral Image Analysis in Biomedical Applications
Milan Gavrilovic, Carolina Wählby
Partners: Irene Weibrecht, Tim Conze, Ola Söderberg, Ulf Landegren, Dept. of Genetics and Pathology, UU
Funding: TN-faculty, UU
Period: 0807-
Abstract: Our previously published novel methods for quantification of
colocalization are applied to a number of projects in collaboration with the
Molecular Tools group at the Dept. of Genetics and Pathology. The aim is
detection of multi-color rolling circle products representing DNA-protein
interactions in wide-field fluorescence microscopy images, as recently
published (Weibrwcht et al., New Biotechnology). In addition, we use the methods
for evaluation of biochemical methods relevant in early stages of specimen
preparation, e.g., estimating the quality of oligonucleotides, as presented in
(Gavrilovic et al., Cytometry).
- Optical Computer Tomography and Automation of Animal Positioning and Brain Cell Counting in Zebrafish
Amin Allalou, Carolina Wählby
Partners: Carlos Pardo, Mehmet F. Yanik, Research Laboratory of Electronics,
Massachusetts Institute of Technology, Cambridge, USA
Funding: TN-faculty, UU, The Swedish Research Council, Collaboration Grant, Medicine
Period: 1009-
Abstract: The research collaboration initiated during 2010 continued with more
visits to MIT, Cambridge, USA, during 2011. A high-throughput platform for
cellular resolution in vivo chemical and genetic screens on zebrafish has been
developed at MIT, Cambridge, USA. The system automatically loads zebrafish
larvae and positions and rotates them for high-speed confocal imaging. The
number of neurons in the fish is of importance to different screens and
therefore a method has been tested to count the number of neurons in certain
regions of the zebrafish brain. In addition, some methods have been developed
to rotate and align the fish correctly before imaging, as published in (Chang
et al., Lab. Chip). The automated rotational positioning also inspired the
development of optical computer tomography methods for accurate and precise 3D
volume rendering of zebrafish embryos, currently under development.
- Multicolor Read-Out Increases the Dynamic Range of in situ PLA
Amin Allalou, Ida-Maria Sintorn, Carolina Wählby
Partners: Carl-Magnus Clausson, Ola Söderberg, Dept. of Genetics and Pathology, UU
Funding: TN-faculty, UU; VINNMER programme, Swedish Governmental Agency for Innovation Systems
Period: 1002-
Abstract: A novel approach to increase the dynamic range of in situ PLA has been developed at Dept. of Genetics and Pathology, UU. Using several probes with different concentrations the dynamic range can be extended significantly. Signal detection previously developed at CBA, UU, (3DSWD) is used to quantify the number of signals in the different concentrations (Clausson et. al. Nature Methods).
- Analysis of Rat Embryos
Khalid Niazi, Ewert Bengtsson
Partners: Mats Nilsson, Lennart Dencker, Div. of Toxicology, Dept. of Pharmaceutical Biosciences, UU; Bill Webster, School of Medical Sciences, University of Sydney, Australia
Funding: Swedish Research Council with Lennart Dencker as PI, COMSATS IIT, Islamabad
Period: 0711-1111
Abstract: Embryo cultures of rodents is an established technique for monitoring adverse effects of chemicals on embryonic development. The cultures are assessed both morphologically looking for changes in the development of various organ structures and through analysis of the heart rate of the embryo where irregularities are a sign of adverse influences of the tested drug.
The heart rate was determined through fully automated image analysis
of video sequences. After finding the heart location in the image
through spatio-temporal image analysis we modeled the movement of the
heart as a sinusoid which was analyzed using Emperical Mode
Decomposition (EMD). We used EMD with slight modification along with
Laplacian Eigenmaps to detect the periodic activity. The normal
embryo's heart activity can easily be detected by local maxima
detection but it becomes a challenging task once the heart activity
becomes abnormal. The developed methods functions well and are being
used by our pharmacological partners.
We also studied how the currently used subjective, semi-quantitative
(categorical, score) assessment of the morphological development could
be made more objective and quantitative. We initially created
automatically extracted descriptors for the different organs that were
to be related to the subjective assessments. The outcome of this needs
to be evaluated further.
This project was presented as part of the PhD thesis by Khalid Niazi that was defended in November 2011.
- Parallax Error Correction in Retinal Image Registration
Khalid Niazi, Bettina Selig, Ewert Bengtsson, Ingela Nyström
Partner: Albert Alm, Dept. of Neurosciences, UU
Funding: COMSATS IIT, Islamabad
Period: 0711-
Abstract: Retinal imaging is one of the main sources in ophthalmology to study
the optical nerve head and the retina. Retinal images are often used
for analyzing, diagnosing and treating a number of diseases of the
human retina. Image registration plays an important role in
determining the progression of retinal illness. In the current
project, we are developing a method which will help in evaluation of
glaucoma progression. We are especially concentrating on correction
of parallax error, which is normally produced due to a change in the
angular position of the camera.
Retinal image registration can be performed between either the full
images or within sub-regions. The movement of vessels makes it
illogical to perform registration between full images. Using a
sub-region which is least effected by vessel movement will present a
true picture of the vessel movement. The vessels inside the optic
disc, which lie close to the origin, move with time and often get
over-exposed during imaging. It has also been reported that the end
of the vessel gets detached from the surface of the retina due to
loss of the nerve fibers, which leaves us to use the area around the
border of the optic disc. We have used the conventional particle
swarm optimization (PSO) algorithm which uses uniform distribution
to update the velocity equation. Subsequently, we have modified the
PSO algorithm which utilizes benefits from the Gaussian and the
uniform distribution, when updating the velocity equation. Which one
of the distributions is selected depends on the direction o f the
cognitive and social components in the velocity equation. This
direction checking and selection of the appropriate distribution
provide the particles with an ability to jump out of local
minima. The registration results achieved by this new version proves
the robustness and its ability to find a global minimum.
This project was presented as part of the PhD thesis by Khalid Niazi that was defended in November 2011.
- Automatic, Quantitative Malignancy Grading of Prostate Cancer using Image Analysis
Ingrid Carlbom, Milan Gavrilovic, Ewert Bengtsson, Jimmy Azar
Partners: Christer Busch, Marene Landström, Dept. of Genetics and Pathology, UU Hospital
Funding: Swedish Research Council
Period: 1001-
Abstract: Prostate cancer diagnosis is based on Gleason grading, which is the
most widely used system for determining the severity of prostate
cancer from tissue samples. However, Gleason grading is highly
subjective with significant variation between experienced
pathologists, which studies show may be as high as 30-40%. We propose
to replace subjective diagnosis of prostate cancer with automatic
severity grading using a combination of tissue staining and image
analysis. The goal of this research is to identify and separate
slow-growing cancer from more aggressive types, thereby helping to reduce needless radical treatment of the disease. Currently about
70% of patients with localized prostate cancer receive aggressive
treatment that does not prolong life but often results in debilitating
side effects.
We have developed an automatic (blind) method, referred
to as the BCD (Blind Color Decomposition) method, for color
decomposition of histological images acquired with a RGB camera. The
method decouples intensity from color information and bases the
decomposition only on the tissue absorption characteristics of a
specific stain. When a stain does not absorb light but rather scatters
light, we automatically remove the areas affected by that stain from
the image prior to color decomposition. By modeling the CCD sensor
noise with statistical techniques, we improve the method's accuracy.
We extend current linear decomposition methods to include stained
tissues where one of their spectral signatures cannot be separated
from all combinations of the other tissues' spectral signatures. The
result of the method is a set of density maps, one for each stained
tissue type, which classifies portions of pixels into the correct
stained tissue thereby reducing aliasing artifacts (See Figure 9). Comparisons with current color decomposition methods demonstrate
that our method outperforms methods in the literature, giving a 92%
median correlation with ground truth as compared to other published
methods that give up to 81% median correlation. The BCD method
produces highly accurate density maps that can be used for
identification of morphological features that are linked to
cancer. This work resulted in a US provisional patent application.
During 2010, we developed a new staining method combining
histochemical and immunohistochemical stains, making it possible to
discriminate between normal glandular structures and infiltrating
cancer. We have continued to develop stains that also give a color
decomposition into density maps which enable highly accurate
measurements of morphological features for determining the malignancy
grade. We are in the process of evaluating several stains using
methods for quantitative determination of the efficacy of stain/tissue
combinations.
We developed a simple, computationally efficient, automated method for
accurate detection and localization of cores in tissue microarray
images (TMAs), which is based on geometric restoration of core shapes
in microarray images without any assumptions on grid geometry. The
method relies on hierarchical clustering in conjunction with the
Davies-Bouldin index for cluster validation in order to estimate the
number of cores present in the image wherefrom we estimate the core
radius and refine this estimate using morphological granulometry. The
final stage of the algorithm reconstructs circular discs from core
sections such that these discs cover the entire region of the core
regardless of the precise shape of the core. The method was tested on
over 32 TMA images comprising over 2300 cores. The results show that
the proposed method is able to reconstruct the location of the cores
without any evidence of localization error even if only a partial core
is included in the slide. Furthermore, an assessment of the
computational efficiency of the algorithm shows it to be far more
efficient than existing methods based on the Hough transform for
circle detection.
Figure 9:
Color decomposition of stained prostate tissue (left), and three density maps (right).
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- A Multidisciplinary Approach to Establish Mechanisms for Mitochondrial DNA Segregation in Human Disease
Amin Allalou, Carolina Wählby
Partners: Nils-Göran Larsson, Dept. of Laboratory Medicine, Karolinska Institute; Mats Nilsson, Chatarina Larsson, Dept. of Genetics and Pathology, UU
Funding: The Swedish Research Council, Collaboration Grant, Medicine
Period: 0801-
Abstract: Mutations of mitochondrial DNA (mtDNA) cause genetic
syndromes with widely varying phenotypes and are also implicated in
many age-associated diseases and the ageing process itself. Our
knowledge of the principles governing segregation of mtDNA mutations
in somatic tissues and in the germ line is very limited. In this
collaborative project we combine a powerful technique for detection
of individual mtDNA molecules with image analysis. We work with a
variety of mouse models and the goal is to develop image analysis
software to do three-dimensional (3D) reconstruction of the
distribution of mutated mtDNA molecules in mammalian tissues. We
want to use this technology to study segregation of mtDNA mutations
in mouse tissues and to study the mtDNA bottleneck by visualizing
the distribution of mutated mtDNA during oogenesis. The ultimate
goal is to study the distribution of mtDNA mutations in embryos and
placenta to establish principles for prenatal diagnosis.
- High Content Analysis (HCA) Method Development for Cellular Screening
Ida-Maria Sintorn
Partners: Adrian Baddeley, Michael Buckley, Leanne Bischof, CSIRO Mathematical and Information Sciences, Australia; Stephen Haggarty, Broad Institute of Harvard and MIT, USA
Funding: S-faculty, SLU; VINNMER programme, Swedish Governmental Agency for Innovation Systems
Period: 0901-
Abstract: In biological research and in the drug development process
when screening for new drugs, HCA systems are often used. Such a
system is a fully automatized microscopy system that acquires
hundreds or thousands of images in an experiment and automatically
extracts information from the image data. In this project we have
developed pre-processing tools to allow for improved comparison of
image content within and across cellular screening experiments. We
also develop methods and new statistical analysis tools for so
called co-culture HCA experiments i.e., when more than one celltype
are cultured together to allow for investigating their interaction
in response to added substrates. During 2011 Sintorn spent three
weeks at the Broad Institute as a guest researcher working on this
project.
- Analysis of Virus Morphology in Electron Microscopy Images
Ida-Maria Sintorn
Partner: Vironova AB, Stockholm
Funding: VINNMER programme, Swedish Governmental Agency for Innovation Systems
Period: 0801-
Abstract: Electron Microscopy allows for studying the shape and
morphology of biological particles such as viruses at the nm level.
This means for example that structural differences between virus
maturation stages, related virus species, wild type virus and virus
treated with a potential drug or a small molecule can be analyzed.
Both external (shape and protein patterns on the virus surface) and
internal structural differences can be analyzed. In this project
methods for efficiently identifying and quantifying such structural
differences are developed.
- Identification of Highly Pathogenic Viruses in Transmission Electron Microscopy Images
Gustaf Kylberg, Ida-Maria Sintorn, Ewert Bengtsson, Gunilla Borgefors
Partners: Vironova AB, Stockholm; Delong Instruments, Brno, Czech Republic; Ali Mirazimi, Kjell-Olof Höglund, Swedish Institute for Communicable Disease Control (SMI); Jan-Olof Strömberg and Joel Andersson, Dept. of Mathematics, KTH
Funding: 2008-2011 Swedish Civil Contingencies Agency (MSB), Swedish Defense Materiel Administration (FMV), Swedish Agency for Innovative Systems (VINNOVA). 2011- Eurostar project E!6143.
Period: 0801-
Abstract: This project aims at automating the virus identification process in high resolution TEM images. This, in combination with project 28 create a rapid, objective, and user independent virus diagnostic system. The identification task consists of method development for segmenting virus particles with different shapes and sizes and extracting descriptive features of both shape and texture to enable the classification into virus species. Texture features such as variants of Local Binary Patterns are being evaluated on virus textures as well as other texture datasets to get a deeper understanding of the discriminant power of the features under different conditions. Figure 10 shows the texture of 15 virus types.
A paper evaluating the segmentation method was accepted to Journal of
Microscopy in 2011. Work on texture features was presented at the
16th Iberoamerican Congress on Pattern Recognition (CIARP) 2011 in
Pucón, Chile.
Figure 10:
Examples of 15 virus textures. Texture features are computed from the area inside the dashed circles. The position of the circles are automatically selected from previously segmented objects.
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- Detection of Regions of Interest for Automated Image Acquisition in Virus Identification
Gustaf Kylberg, Ida-Maria Sintorn, Ewert Bengtsson, Gunilla Borgefors
Partners: Vironova AB, Stockholm; Delong Instruments, Brno, Czech Republic; Ali Mirazimi, Kjell-Olof Höglund, Swedish Institute for Infectious Communicable Control (SMI); Gun Frisk and Monika Hodik, Dept. of Immunology, Genetics and Pathology, UU
Funding: 2008-2011 Swedish Civil Contingencies Agency (MSB), Swedish Defense Materiel Administration (FMV), Swedish Agency for Innovative Systems (VINNOVA). 2011- Eurostar project E!6143
Period: 0801-
Abstract: Transmission electron microscopy (TEM) is an important
virus diagnostic tool. The main drawback is that an expert in virus
appearance in electron microscopy needs to perform the analysis at
the microscope, an often very time consuming task.
The project aim is to develop methods for a multi-scale analysis at
the microscope to automatically acquire highly magnified images of
possible virus particles. This is an important step towards automating
the virus identification process and thereby creating a rapid,
objective, and user independent virus diagnostic system. By
introducing the multi-scale approach the search area where highly
magnified images need to be acquired is estimated to be reduced with
more than 99.99%.
As of mid 2011 Delong Instruments has joined the project. They will
develop a novel bench-top low-voltage TEM where the methods for
automated acquisition will be incorporated. This work will intensify
the development of methods for the automatic acquisition of images.
- Analysis of Male Reproductive Tract Morphology in Reproductive Toxicology
Azadeh Fakhrzadeh, Cris Luengo, Gunilla Borgefors
Partners: Ellinor Spörndly-Nees, Lena Holm, Dept. of Anatomy, Physiology and Biochemistry, SLU
Funding: SLU (KoN)
Period: 1009-
Abstract: Reproductive toxicology is the study of chemicals and their effects on
the reproductive system of humans and animals. In reproductive
toxicology, there is a strong need to detect structural differences in
organs that often have both a complex microscopic structure and
function. This problem is further complicated because standard
techniques are based on the examination of two-dimensional sections of
a three-dimensional structure. The aim of this project is to develop
methods to objectively describe microscopic structures of male
reproductive organs and to test these in reproductive toxicology
research. The project is comparative and includes studies of organs
from rooster and mink. We are developing automatic and interactive
methods to analyze the relevant structures in the histology images of
testis. We have constructed an automatic method to delineate the
seminiferous tubule border and lumen, see Figure 11. We use a level
set based active contour method to delineate the lumen border and
classical classification scheme to detect the seminiferous border.
Figure 11:
Cross section of the testis, H & E stained. Our algorithm delineates the seminiferous tubules border (in black) and lumen border (in red).
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- New Objective Quantitative Analysis Techniques for
Quantification of Tissue Regeneration Around Medical Devices
Hamid Sarve, Joakim Lindblad, Vladimir Curic, Gunilla Borgefors
Partners: Carina Johansson, Dept. of Clinical Medicine, Örebro
University/Institute of Odontology, The Sahlgrenska Academy, Gteborg; Nataša Sladoje, Faculty of Technical Sciences,
University of Novi Sad, Serbia
Funding: Swedish Research Council; S-faculty, SLU
Period: 0503-
Abstract: With an aging and increasingly osteoporotic population, bone implants
are becoming more important to ensure the quality of life. In order to
evaluate how tissue reacts on implants, the interface at the implant
and tissue must be studied. Today, this is done manually in a
microscope which is a costly and time-consuming procedure.
The aim of this project is to develop automatic image analysis methods
for evaluating images of the interface region of tissue and implant.
These methods would provide faster and more objective measurements on
how well the implant is integrated in the bone compared to today's
manual methods. The analysis involves segmentation of the images into
different tissue-types and quantifying bone contact length, area and
volume.
The project encompasses parallel development and comparison of methods
for 2D analysis of histological sections as well as 3D analysis of
SRCT volumes. Within the project, methods for segmentation and
feature extraction have been developed for both 2D histological
sections, and 3D SRCT volumes. To facilitate comparison of
results from the two imaging modalities, a 2D-3D image registration
method has also been developed. Furthermore, another significant
contribution has been the development of methods for extraction of the
3D features from the 3D volume data. A thesis, titled Evaluation of
Osseointegration using Image Analysis and Visualization of 2D and 3D
Image Data, which was based on the methods described above were
defended by Hamid Sarve in 2011.
- Assessing Bone Implant Integration From Synchrotron micro-CT Data
Hamid Sarve, Joakim Lindblad, Gunilla Borgefors
Partners: Carina Johansson, Dept. of Clinical Medicine, Örebro
UniversityDept. of Clinical Medicine, Örebro
University/Institute of Odontology, The Sahlgrenska Academy, Gteborg; AstraTech, Mölndal
Funding: The Knowledge Foundation
Period: 0906-
Abstract: This project aims to develop new techniques for interactive 3D
visualization of bone anchored implants in order to facilitate the
understanding of the mechanisms of implant integration. To enable good
communication between the people involved in development, production
and use of medical devices - computer scientists, material
scientists, and medical doctors - each with their own special
knowledge, it is of highest importance to provide a common visual
platform for a mutual understanding of the problems of implant
integration. Being able to actually see the 3D structure around the
implant for the first time will inspire new measures of the implant
integration quality.
We base the visualization on data from non-destructive 3D SRCT
imaging; this technique yields more accurate tomographic
reconstruction at higher resolution compared to standard CT.
Furthermore, SRCT-imaging is more suitable for samples containing
metal, as the artefacts caused by the metal is significantly lower
compared to traditional CT. Existing visualization software are not
really useful for this type of complex and highly detailed data,
requiring the development of special purpose methods and software.
The combination of this project and project 30 will
improve both quantitative and qualitative analysis of bone implant
integration and thereby support the development of more effective
implants and diminish the number of malfunctioning devices.Two new
methods for visualizing SRCT-scanned volume samples were
presented at the International Conference on Computer Vision and
Graphics (ICCVG'10); one being an animation that follows the implant
thread and extracts information about the bone-implant integration
over the whole sample and the other a 2D unfolding that displays a
flattened version of the implant surface, with feature information
projected onto it, providing a direct overview of the implant
integration (see Figure 12). The methods were applied on
real clinical data; they were applied in a case study involving
retrieved human oral implants. As the case study showed, the use of 3D
techniques highlighted the complexity of osseointegration and provided
information other than the 2D analysis on histological images. The
visualization techniques were further discussed in Hamid Sarve's
thesis, Evaluation of Osseointegration using Image Analysis and
Visualization of 2D and 3D Image Data, defended in 2011.
Figure 12:
(Left) Rendered surface of the implant with bone tissue volume in the
region of interest superimposed. (Right) The unfolded surface, where
the blue regions indicate high concentration of bone tissue (see the
bar to the right). White dashed lines show the peaks of the threads.
The vertical line indicates the corresponding angles in the two
images.
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- Combating Breast Cancer by Digital Pathology
Andreas Kårsnäs, Robin Strand, Ewert Bengtsson
Partners: Visiopharm, Hørsholm, Denmark; Clinical Pathology Division, Vejle hospital, Vejle, Denmark
Funding: NordForsk Private Public Partnership PhD Programme and Visiopharm
Period: 0909-
Abstract: The results of analyses of tissue biopsies by pathologists
are crucial for breast cancer patients. In particular, the precision
of a patient's prognosis, and the ability to predict the
consequences of various treatment opportunities before actually
exposing the cancer patient, depend on the detection and
quantification of biomarkers in tissue sections by
microscopy. Experience from the last decade has revealed that manual
detection and quantification of biomarkers by microscopy of tissue
biopsies is highly dependent on the competencies and stamina of the
individual pathologist. The aim of the present PhD project is to
develop software-based algorithms that can facilitate the workflow
and ensure objective and more precise results of the quantitative
microscopy procedures in breast cancer.
