KEYWORDS: Image segmentation, Skin, 3D modeling, Image classification, Education and training, In vitro testing, Histopathology, Data modeling, Deep learning, Tunable filters
Cell-based in vitro skin models are an effective method for testing new medical compounds without any animal harming in the process. Histology serves as a cornerstone for evaluating in vitro models, providing critical insights into their structural integrity and functionality. The recently published BSGC score is a method to assess the quality of in vitro epidermal models, based on visual examination of histopathological images. However, this is very time-consuming and requires a high level of expertise. Therefore, this paper presents a method for automatic evaluation of three-dimensional in vitro epidermal models that involves segmentation and classification of epidermal layers in cross-sectional histopathological images. The input images are first pre-processed and in an initial classification step low-quality skin models are filtered. Subsequently, the individual epidermal strata are segmented and a masked image is generated for each stratum. The strata are scored individually using the masked images with a classification network per stratum. Finally the individual scores are merged into an overall weighted score per image. With an accuracy of 81% for the overall scoring the method provides promising results and allows for significant time savings and less subjectivity compared to the manual scoring process.
Nasal septal deviations are a well-known and widespread problem. According to the American Academy of Otolaryngology 80% of the population have a nasal septal deviation. Its level of severity can range from the person not being aware of it to respiratory obstruction and choking. It is therefore necessary to distinguish those patients at risk. For a proper diagnosis, the amount and location of the deviation have to be considered, but also the shape and changes in the surrounding turbinates. The segmentation of the structures of interest is an important step to reduce subjectivity in the diagnosis. Unfortunately, due to their variable and tortuous shape manual segmentation is time consuming. In this paper, the first method for the automatic segmentation of the structures in the nasal cavity and ethmoidal sinus is presented. A coupled shape model of the nasal cavity and paranasal sinus regions is trained and used to detect the corresponding regions in new CT images. The nasal septum is then segmented using a novel slice-based propagation technique. This segmentation allows the additional separation and segmentation of the left and right nasal cavities and ethmoidal sinuses and their structures by means of an adaptive thresholding with varying boundary sizes. The method has been evaluated in 10 CT images obtaining promising results for the nasal septum (DICE: 87.71%) and for the remaining structures (DICE: 72.01% - 73.01%). Based on the resulting segmentations, a web-based diagnosis tool has been designed to quantify the septal deviation using three metrics proposed by clinical experts.
Misalignment of teeth or jaws can impact the ability to chew or speak, increase the risk of gum disease or tooth decay, and potentially influence a person’s (psychological) well-being. Orthodontic treatments of misaligned teeth are complex procedures that employ dental braces to apply forces in order to move the teeth or jaws to their correct position. Photographs are typically used to document the treatment. An automatic analysis of those photographs could support the decision making and monitoring process. In this paper, we propose an automatic model-based end-to-end 3-D reconstruction approach of the teeth from five photographs with predefined viewing directions (i.e. the photographs used in orthodontic treatment documentation). It uses photo- or view-specific 2-D coupled shape models to extract the teeth contours from the images. The shape reconstruction is then carried out by a deformation-based reconstruction approach that utilizes 3-D coupled shape models and minimizes a silhouette-based loss. The optimal model parameters are determined by an optimization which maximizes the overlaps between the projected 2-D outlines of individual teeth of the 3-D model and the contours extracted from the corresponding photograph. After that the point displacements between the projected outline and segmented contour are used to iteratively deform the 3-D shape model of each tooth for all five views. Back-projection into shape space ensures that the 3-D coupled shape model consists of (statistically) valid teeth. Evaluation on 22 data sets shows promising results with an average symmetric surface distance of 0.848mm and an average DICE coefficient of 0.659.
The autoimmune disease Juvenile Idiopathic Arthritis (JIA) affects children of under 16 years and leads to the symptom of inflamed synovial membranes in affected joints. In clinical practice, characteristics of these inflamed membranes are used to stage the disease progression and to predict erosive bone damage. Manual outlining of inflammatory regions in each slide of a MRI dataset is still the gold standard for detection and quantification, however, this process is very tiresome and time-consuming. In addition, the inter- and intra-observer variability is a known problem of human annotators. We have developed the first method to detect inflamed regions in and around major joints in the human ankle. First, we use an adapted coupled shape model framework to segment the ankle bones in a MRI dataset. Based on these segmentations, joints are defined as locations where two bones are particularly close to each other. A number of potential inflammation candidates are generated using multi-level thresholding. Since it is known that inflamed synovial membranes occur in the proximity of joints, we filter out structures with similar intensities such as vessels and tendons sheaths using not only a vesselness filter, but also their distance to the joints and their size. The method has been evaluated on a set of 10 manually annotated clinical MRI datasets and achieved the following results: Precision 0.6785 ± 0.1584, Recall 0.5388 ± 0.1213, DICE 0.5696 ± 0.0976.
Ultrasound (U/S) is a fast and non-expensive imaging modality that is used for the examination of various anatomical structures, e.g. the kidneys. One important task for automatic organ tracking or computer-aided diagnosis is the identification of the organ region. During this process the exact information about the transducer location and orientation is usually unavailable. This renders the implementation of such automatic methods exceedingly challenging. In this work we like to introduce a new automatic method for the detection of the kidney in 3D U/S images. This novel technique analyses the U/S image data along virtual scan lines. Here, characteristic texture changes when entering and leaving the symmetric tissue regions of the renal cortex are searched for. A subsequent feature accumulation along a second scan direction produces a 2D heat map of renal cortex candidates, from which the kidney location is extracted in two steps. First, the strongest candidate as well as its counterpart are extracted by heat map intensity ranking and renal cortex size analysis. This process exploits the heat map gap caused by the renal pelvis region. Substituting the renal pelvis detection with this combined cortex tissue feature increases the detection robustness. In contrast to model based methods that generate characteristic pattern matches, our method is simpler and therefore faster. An evaluation performed on 61 3D U/S data sets showed, that in 55 cases showing none or minor shadowing the kidney location could be correctly identified.
The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.
Malignant melanoma is a form of skin cancer, with increasing incidence worldwide. Early diagnosis is crucial for the prognosis and treatment of the disease. The objective of this study is to develop a novel animal model of melanoma and apply a combination of the non-invasive imaging techniques acoustic microscopy, infrared (IR) and Raman spectroscopies, for the detection of developing tumors. Acoustic microscopy provides information about the 3D structure of the tumor, whereas, both spectroscopic modalities give qualitative insight of biochemical changes during melanoma development. In order to efficiently set up the final devices, propagation of ultrasonic and electromagnetic waves in normal skin and melanoma simulated structures was performed. Synthetic and grape-extracted melanin (simulated tumors), endermally injected, were scanned and compared to normal skin. For both cases acoustic microscopy with central operating frequencies of 110MHz and 175MHz were used, resulting to the tomographic imaging of the simulated tumor, while with the spectroscopic modalities IR and Raman differences among spectra of normal and melanin- injected sites were identified in skin depth. Subsequently, growth of actual tumors in an animal melanoma model, with the use of human malignant melanoma cells was achieved. Acoustic microscopy and IR and Raman spectroscopies were also applied. The development of tumors at different time points was displayed using acoustic microscopy. Moreover, the changes of the IR and Raman spectra were studied between the melanoma tumors and adjacent healthy skin. The most significant changes between healthy skin and the melanoma area were observed in the range of 900-1800cm-1 and 350-2000cm-1, respectively.
This paper presents a novel segmentation method for the joint segmentation of individual bones in CT- or CT/MR- head and neck images. It is based on an articulated atlas for CT images that learned the shape and appearance of the individual bones along with the articulation between them from annotated training instances. First, a novel dynamic adaptation strategy for the atlas is presented in order to increase the rate of successful adaptations. Then, if a corresponding CT image is available the atlas can be enriched with personalized information about shape, appearance and size of the individual bones from that image. Using mutual information, this personalized atlas is adapted to an MR image in order to propagate segmentations. For evaluation, a head and neck bone atlas created from 15 manually annotated training images was adapted to 58 clinically acquired head andneck CT datasets. Visual inspection showed that the automatic dynamic adaptation strategy was successful for all bones in 86% of the cases. This is a 22% improvement compared to the traditional gradient descent based approach. In leave-one-out cross validation manner the average surface distance of the correctly adapted items was found to be 0.6 8mm. In 20 cases corresponding CT/MR image pairs were available and the atlas could be personalized and adapted to the MR image. This was successful in 19 cases.
To know the exact location of the internal structures of the organs, especially the vasculature, is of great importance for the clinicians. This information allows them to know which structures/vessels will be affected by certain therapy and therefore to better treat the patients. However the use of internal structures for registration is often disregarded especially in physical based registration methods. In this paper we propose an algorithm that uses finite element methods to carry out a registration of liver volumes that will not only have accuracy in the boundaries of the organ but also in the interior. Therefore a graph matching algorithm is used to find correspondences between the vessel trees of the two livers to be registered. In addition to this an adaptive volumetric mesh is generated that contains nodes in the locations in which correspondences were found. The displacements derived from those correspondences are the input for the initial deformation of the model. The first deformation brings the internal structures to their final deformed positions and the surfaces close to it. Finally, thin plate splines are used to refine the solution at the boundaries of the organ achieving an improvement in the accuracy of 71%. The algorithm has been evaluated in CT clinical images of the abdomen.
Image guided therapy is a natural concept and commonly used in medicine. In anesthesia, a common task is the injection of an anesthetic close to a nerve under freehand ultrasound guidance. Several guidance systems exist using electromagnetic tracking of the ultrasound probe as well as the needle, providing the physician with a precise projection of the needle into the ultrasound image. This, however, requires additional expensive devices. We suggest using optical tracking with miniature cameras attached to a 2D ultrasound probe to achieve a higher acceptance among physicians. The purpose of this paper is to present an intuitive method to calibrate freehand ultrasound needle guidance systems employing a rigid stereo camera system. State of the art methods are based on a complex series of error prone coordinate system transformations which makes them susceptible to error accumulation. By reducing the amount of calibration steps to a single calibration procedure we provide a calibration method that is equivalent, yet not prone to error accumulation. It requires a linear calibration object and is validated on three datasets utilizing di erent calibration objects: a 6mm metal bar and a 1:25mm biopsy needle were used for experiments. Compared to existing calibration methods for freehand ultrasound needle guidance systems, we are able to achieve higher accuracy results while additionally reducing the overall calibration complexity. Ke
C-arm fluoroscopy is used for guidance during several clinical exams, e.g. in bronchoscopy to locate the bronchoscope inside the airways. Unfortunately, these images provide only 2D information. However, if the C-arm pose is known, it can be used to overlay the intrainterventional fluoroscopy images with 3D visualizations of airways, acquired from preinterventional CT images. Thus, the physician's view is enhanced and localization of the instrument at the correct position inside the bronchial tree is facilitated. We present a novel method for C-arm pose estimation introducing a marker-based pattern, which is placed on the patient table. The steel markers form a pattern, allowing to deduce the C-arm pose by use of the projective invariant cross-ratio. Simulations show that the C-arm pose estimation is reliable and accurate for translations inside an imaging area of 30 cm x 50 cm and rotations up to 30°. Mean error values are 0.33 mm in 3D space and 0.48 px in the 2D imaging plane. First tests on C-arm images resulted in similarly compelling accuracy values and high reliability in an imaging area of 30 cm x 42.5 cm. Even in the presence of interfering structures, tested both with anatomy phantoms and a turkey cadaver, high success rates over 90% and fully satisfying execution times below 4 sec for 1024 px × 1024 px images could be achieved.
Accurate multi-material mesh generation is necessary for many applications, e.g. image-guided surgery, in which
precision is important. For this application, it is necessary to enhance conventional algorithms with physiological
information that adds accuracy to the results. There are several approaches working on the generation of such
meshes. However, state of the art approaches show inaccuracies in the areas in which thin structures are,
e.g. liver vasculature. These algorithms are not able to detect the vessels in areas in which they are narrow
and they assign their elements to wrong materials, e.g., parenchyma. We propose to extend two state of the
art algorithms, namely that by Boltcheva et al. and that by Pons et al. and enhance them making use of
the skeleton of these structures to solve this problem. By analyzing the mesh generated by the aforementioned
algorithms one can find several intersections between the mesh belonging to the vessels and the skeleton, showing
that some elements must be mismatched. We evaluate the proposed algorithm in 23 clinical datasets of the liver,
in which we previously segmented parenchyma and vessels. For quantitative evaluation, the meshes generated
with and without skeleton information are compared. The improvements are shown by means of intersection
number, volume and length differences of the vasculature mesh using the different methods. The results show
an improvement of 65% for the number of intersections, 4% for the volume and 22% for the length.
Modern volumetric imaging techniques such as CT or MRI, aid in the understanding of a patient's anatomy and
pathologies. Depending on the medical use case, various anatomical structures are of interest. Blood vessels
play an important role in several applications, e.g. surgical planning. Manual delineation of blood vessels in
volumetric images is error prone and time consuming. Automated vessel segmentation is a challenging problem
due to acquisition-dependent problems such as noise, contrast, spatial resolution, and artifacts. In this paper, a
vessel segmentation method is presented that combines a wavefront propagation technique with Hessian-based
vessel enhancement. The latter has proven its usefulness as a preprocessing step to detect tubular structures
before the actual segmentation is carried out. The former allows for an ordered growing process, which enables
topological analysis. The contribution of this work is as follows. 1. A new vessel enhancement filter for tubular
structures based on the Laplacian is proposed, 2. a wavefront propagation technique is proposed that prevents
leaks by imposing a threshold on the maximum number of voxels that the propagating front must contain, and 3.
a volumetric hole filling method is proposed to filll holes, bays, and tunnels which are caused at locations where
the tubular structure assumption is violated. The proposed method reduces approximately 50% of the necessary
eigenvalue calculations for vessel enhancement and prevents leaks starting at small spots, which usually occur
using standard region growing. Qualitative and quantitative evaluation based on several metrics (statistical
measures, dice and symmetric average surface distance) is presented.
Multimodal registration of intraoperative ultrasound and preoperative contrast enhanced computed tomography (CT) imaging is the basis for image guided percutaneous hepatic interventions. Currently, the surgeon manually performs a rigid registration using vessel structures and other anatomical landmarks for visual guidance. We have previously presented our approach for an automation of this intraoperative registration step based on the definition of bijective correspondences between the vessel structures using an automatic graph matching.1 This paper describes our method for refinement and expansion of the matched vessel graphs, resulting in a high number of bijective correspondences. Based on these landmarks, we could extend our method to a fully deformable registration. Our system was applied successfully on CT and ultrasound data of nine patients, which are studied in this paper. The number of corresponding vessel points could be raised from a mean of 9.6 points after the graph matching to 70.2 points using the presented refinement method. This allows for the computation of a smooth deformation field. Furthermore, we can show that our deformation calculation raises the registration accuracy for 3 of the 4 chosen target vessels in pre-/postoperative CT with a mean accuracy improvement of 44%.
Motion tracking for head motion compensation in MRI has been a research topic for several years. However,
literature is not giving much attention to the calibration of such setups. We present a method to calibrate the
coordinate systems of a stereo-optical camera setup mounted to the MRI head coil. Though using a simple setup
and visible instead of infrared light for tracking, it is possible to achieve a sub-millimeter tracking precision.
Blue water-filled spheres are positioned throughout the whole MRI imaging volume and detected in images
of the tracking cameras as well as MRI scans. In order to register the coordinate systems of both camera system
and MRI scanner, a heuristic-enhanced brute-force approach is used to match detected spheres in the different
images. Then, a rigid transformation is calculated and applied to the cameras' external parameters to align the
coordinate systems.
The precision of our setup was evaluated using leave-one-out cross validation both for the camera calibration
and the scanner coordinate system registration. We found that the cameras' locations and orientations are
correct within 0:03mm and 0:03°, using a number of 45 spheres. Evaluation of the MRI coordinate system
registration showed an average reprojection error of 1:1 mm.
Influence of a feature point jitter of 0:5 px is 0:03mm for a point close to the cameras and 0:3mm for a point
close to the back of the patient's head. Tracked poses are correct within 0:17mm and 0:001.°
Automatic detection of anatomical structures and regions in 3D medical images is important for several computer aided diagnosis tasks. In this work, a new method for simultaneous detection of multiple anatomical areas is proposed. The method consists of two steps: first, single rectangular region candidates are detected independently using 3D variants of Histograms of Oriented Gradients (HOG) features. These features are robust against small changes between regions in rotation and scale which typically occur between different individuals. In a second step, the positions of the detected candidates are refined by incorporating a body landmark network that exploits anatomical relations between different structures. The landmark network consists of a principle component based statistical modeling of the relative positions between the detected regions in training images. The method has been evaluated on thoracic/abdominal CT images of the portal venous phase. In 216 CT images, eight different structures have been trained. Results show an increase in performance using the combination of HOGs and the landmark network in comparison to using independent classifiers without anatomical relations.
KEYWORDS: Lawrencium, Magnetic resonance imaging, 3D image reconstruction, Reconstruction algorithms, 3D image processing, Algorithms, Image quality, Super resolution, Data modeling, Systems modeling
Super-resolution reconstruction (SRR) algorithms are used for getting high-resolution (HR) data from low-resolution
observations. In Maximum a posteriori (MAP) based SRR the observation model is employed for
estimating a HR image that best reproduces the two low-resolution input data sets. The parameters of the
prior play a significant role in the MAP based SRR. This work concentrates on the investigation of the influence
of one such parameter, called temperature, on the reconstructed 3D MR images. The existing approaches on
SRR in 3D MR images use a constant value for this parameter. We use a cooling schedule similar to simulated
annealing for computing the value of the temperature parameter at each iteration of the SRR. We have used
3D MR cardiac data sets in our experiments and have shown that the iterative computation of the temperature
which resembles simulated annealing delivers better results.
KEYWORDS: Lawrencium, Image resolution, Super resolution, Image quality, Signal to noise ratio, CT reconstruction, Image restoration, Speckle, Visualization, Medical imaging
Magnetic Resonance Imaging and Computed Tomography usually provide highly anisotropic image data, so that
the resolution in the slice-selection direction is poorer than in the in-plane directions. An isotropic high-resolution
image can be reconstructed from two orthogonal scans of the same object. While combining the different data
sets, all input data are usually equally weighted, without considering the fidelity level of each input information.
In this paper we introduce a novel super-resolution method, which considers the fidelity level of each input data
by introducing an adaptive confidence map. Experimental results on simulated and real data sets have shown
the improved accuracy of reconstructed images, whose resolution approximate the original in-plane resolution in
all directions. The quality of the reconstructed high resolution image was improved for noiseless input data sets,
and even in the presence of different noise types with a low peak signal to noise ratio.
KEYWORDS: Arteries, Computer simulations, Angiography, Data modeling, Finite element methods, 3D modeling, Image segmentation, Systems modeling, Computer architecture, Data acquisition
Selecting the best catheter prior to coronary angiography significantly reduces the exposure time to radiation
as well as the risk of artery punctures and internal bleeding. In this paper we describe a simulation based
technique for selecting an optimal catheter for right coronary angiography using the Simulation Open Framework
Architecture (SOFA). We simulate different catheters in a patient-specific arteries model, obtain final placement
of different catheters and suggest an optimally placed catheter. The patient-specific arteries model is computed
from the patient image data acquired prior to the intervention and the catheters are modeled using Finite Element
Method (FEM).
KEYWORDS: Image segmentation, Liver, 3D modeling, Statistical modeling, Image processing algorithms and systems, Principal component analysis, Computed tomography, Data modeling, Image filtering, 3D image processing
Active Shape Models (ASMs) are a popular family of segmentation algorithms which combine local appearance
models for boundary detection with a statistical shape model (SSM). They are especially popular in medical
imaging due to their ability for fast and accurate segmentation of anatomical structures even in large and noisy
3D images. A well-known limitation of ASMs is that the shape constraints are over-restrictive, because the
segmentations are bounded by the Principal Component Analysis (PCA) subspace learned from the training
data. To overcome this limitation, we propose a new energy minimization approach which combines an external
image energy with an internal shape model energy. Our shape energy uses the Distance From Feature Space
(DFFS) concept to allow deviations from the PCA subspace in a theoretically sound and computationally fast
way. In contrast to previous approaches, our model does not rely on post-processing with constrained free-form
deformation or additional complex local energy models. In addition to the energy minimization approach, we
propose a new method for liver detection, a new method for initializing an SSM and an improved k-Nearest
Neighbour (kNN)-classifier for boundary detection. Our ASM is evaluated with leave-one-out tests on a data
set with 34 tomographic CT scans of the liver and is compared to an ASM with standard shape constraints.
The quantitative results of our experiments show that we achieve higher segmentation accuracy with our energy
minimization approach than with standard shape constraints.nym
Dual-energy CT allows for a better material differentiation than conventional CT. For the purpose of osteoporosis
diagnosis, a detection of regions with lowered bone mineral density (BMD) is of high clinical interest. Based on
an existing biophysical model of the trabecular bone in vertebrae a new method for directly highlighting those
low density regions in the image data has been developed. For this, we combine image data acquired at 80 kV
and 140 kV with information about the BMD range in different vertebrae and derive a method for computing a
color enhanced image which clearly indicates low density regions. An evaluation of our method which compares
it with a quantitative method for BMD assessment shows a very good correspondence between both methods.
The strength of our method lies in its simplicity and speed.
During coronary artery angiography, a catheter is used to inject a contrast dye into the coronary arteries. Due
to the anatomical variation of the aorta and the coronary arteries in different humans, one common catheter
cannot be used for all patients. The cardiologists test different catheters for a patient and select the best catheter
according to the patient's anatomy. This procedure is time consuming and there is a slight chance of cancer from
excessive exposure to radiation. To overcome these problems, we propose a computer aided catheter selection
procedure. In this paper we present our approach for the angiography of the Right Coronary Artery (RCA).
Our approach involves segmentation of the aorta and coronary arteries, finding the centerline and computing the
Curve Angle (CA) and Curve Length (CL) between the aorta and the coronary arteries. We then compute CA
and CL of catheters and suggest a catheter with the closest CA and CL with respect to the aorta's and coronary
arteries' CA and CL. This solution avoids testing of many catheters during catheterization. The cardiologist
already gets the recommendation about the optimal catheter for the patient prior to the intervention.
Statistical shape models play a very important role in most modern medical segmentation frameworks. In this
work we propose an extension to an existing approach for statistical shape model generation based on manual
mesh deformation. Since the manual acquisition of ground truth segmentation data is a prerequisite for shape
model creation, we developed a method that integrates a solution to the landmark correspondence problem in
this particular step. This is done by coupling a user guided mesh adaptation for ground truth segmentation with
a simultaneous real time optimization of the mesh in order to preserve point correspondences. First, a reference
model with evenly distributed points is created that is taken as the basis of manual deformation. Afterwards
the user adapts the model to the data set using a 3D Gaussian deformation of varying stiffness. The resulting
meshes can be directly used for shape model construction. Furthermore, our approach allows the creation of shape
models of arbitrary topology. We evaluate our method on CT data sets of the kidney and 4D MRI time series
images of the cardiac left ventricle. A comparison with standard ICP-based and population-based optimization
based correspondence algorithms showed better results both in terms of generalization capability and specificity
for the model generated by our approach. The proposed method can therefore be used to considerably speed
up and ease the process of shape model generation as well as remove potential error sources of landmark and
correspondence optimization algorithms needed so far.
Prior knowledge can highly improve the accuracy of segmentation algorithms for 3D medical images. A popular
method for describing the variability of shape of organs are statistical shape models. One of the greatest challenges
in statistical shape modeling is to compute a representation of the training shapes as vectors of corresponding
landmarks, which is required to train the model. Many algorithms for extracting such landmark vectors work
on parameter space representations of the unnormalized training shapes. These algorithms are sensitive to
inconsistent parameterizations: If corresponding regions in the training shapes are mapped to different areas of
the parameter space, convergence time increases or the algorithms even fail to converge. In order to improve
robustness and decrease convergence time, it is crucial that the training shapes are parameterized in a consistent
manner. We present a novel algorithm for the construction of groupwise consistent parameterizations for a set
of training shapes with genus-0 topology. Our algorithm firstly computes an area-preserving parameterization
of a single reference shape, which is then propagated to all other shapes in the training set. As the parameter
space propagation is controlled by approximate correspondences derived from a shape alignment algorithm,
the resulting parameterizations are consistent. Additionally, the area-preservation property of the reference
parameterization is likewise propagated such that all training shapes can be reconstructed from the generated
parameterizations with a simple uniform sampling technique. Though our algorithm considers consistency as an
additional constraint, it is faster than computing parameterizations for each training shape independently from
scratch.
In cardiac MR images the slice thickness is normally greater than the pixel size within the slices. In general,
better segmentation and analysis results can be expected for isotropic high-resolution (HR) data sets. If two
orthogonal data sets, e. g. short-axis (SA) and long-axis (LA) volumes are combined, an increase in resolution
can be obtained.
In this work we employ a super-resolution reconstruction (SRR) algorithm for computing high-resolution data
sets from two orthogonal SA and LA volumes. In contrast to a simple averaging of both data in the overlapping
region, we apply a maximum a posteriori approach. There, an observation model is employed for estimating an
HR image that best reproduces the two low-resolution input data sets.
For testing the SRR approach, we use clinical MRI data with an in-plane resolution of 1.5 mm×1.5 mm and
a slice thickness of 8 mm. We show that the results obtained with our approach are superior to currently used
averaging techniques. Due to the fact that the heart deforms over the cardiac cycle, we investigate further, how
the replacement of a rigid registration by a deformable registration as preprocessing step improves the quality
of the final HR image data. We conclude that image quality is dramatically enhanced by applying an SRR
technique especially for cardiac MR images where the resolution in slice-selection direction is about five times
lower than within the slices.
In many applications for minimal invasive surgery the acquisition of intra-operative medical images is helpful if not absolutely necessary. Especially for Brachytherapy imaging is critically important to the safe delivery of the therapy. Modern computed tomography (CT) and magnetic resonance (MR) scanners allow minimal invasive procedures to be performed under direct imaging guidance. However, conventional scanners do not have real-time imaging capability and are expensive technologies requiring a special facility. Ultrasound (U/S) is a much cheaper and one of the most flexible imaging modalities. It can be moved to the application room as required and the physician sees what is happening as it occurs.
Nevertheless it may be easier to interpret these 3D intra-operative U/S images if they are used in combination with less noisier preoperative data such as CT. The purpose of our current investigation is to develop a registration tool for automatically combining pre-operative CT volumes with intra-operatively acquired 3D U/S datasets. The applied alignment procedure is based on the information theoretic approach of maximizing the mutual information of two arbitrary datasets from different modalities.
Since the CT datasets include a much bigger field of view we introduced a bounding box to narrow down the region of interest within the CT dataset. We conducted a phantom experiment using a CIRS Model 53 U/S Prostate Training Phantom to evaluate the feasibility and accuracy of the proposed method.
Medical imaging is nowadays much more than only providing data for diagnosis. It also links 'classical' diagnosis to modern forms of treatment such as image guided surgery. Those systems require the identification of organs, anatomical regions of the human body etc., i. e. the segmentation of structures from medical data sets. The
algorithms used for these segmentation tasks strongly depend on the object to be segmented. One structure which plays an important role in surgery planning are vessels that are found everywhere in
the human body. Several approaches for their extraction already exist. However, there is no general one which is suitable for all types of data or all sorts of vascular structures. This work presents a new algorithm for the segmentation of vessels. It can be classified as a skeleton-based approach working on 3D data sets, and has been designed for a reliable segmentation of coronary arteries. The algorithm is a semi-automatic extraction technique requiring the definition of the start and end the point of the (centerline) path to be found. A first estimation of the vessel's centerline is calculated and then corrected iteratively by detecting the vessel's border perpendicular to the centerline. We used contrast enhanced CT data sets of the thorax for testing our approach. Coronary arteries have been extracted from the data sets using the 'corkscrew algorithm' presented in this work. The segmentation turned out to be robust even if moderate breathing artifacts were present in the data sets.
Brachytherapy is the treatment method of choice for patients with a tumor relapse after a radiation therapy
with external beams or tumors in regions with sensitive surrounding organs-at-risk, e. g. prostate tumors. The
standard needle implantation procedure in brachytherapy uses pre-operatively acquired image data displayed as
slices on a monitor beneath the operation table. Since this information allows only a rough orientation for the
surgeon, the position of the needles has to be verified repeatedly during the intervention.
Within the project Medarpa a transparent display being the core component of a medical Augmented
Reality (AR) system has been developed. There, pre-operatively acquired image data is displayed together with
the position of the tracked instrument allowing a navigated implantation of the brachytherapy needles. The
surgeon is enabled to see the anatomical information as well as the virtual instrument in front of the operation
area. Thus, the Medarpa system serves as "window into the patient".
This paper deals with the results of first clinical trials of the system. Phantoms have been used for evaluating
the achieved accuracy of the needle implantation. This has been done by comparing the output of the system
(instrument positions relative to the phantom) with the real positions of the needles measured by means of a
verification CT scan.
All over the world 20% of men are expected to develop prostate cancer sometime in his life. In addition to surgery - being the traditional treatment for cancer - the radiation treatment is getting more popular. The most interesting radiation treatment regarding prostate cancer is Brachytherapy radiation procedure. For the safe delivery of that therapy imaging is critically important. In several cases where a CT device is available a combination of the information provided by CT and 3D Ultrasound (U/S) images offers advantages in recognizing the borders of the lesion and delineating the region of treatment. For these applications the CT and U/S scans should be registered and fused in a multi-modal dataset.
Purpose of the present development is a registration tool (registration, fusion and validation) for available CT volumes with 3D U/S images of the same anatomical region, i.e. the prostate. The combination of these two imaging modalities interlinks the advantages of the high-resolution CT imaging and low cost real-time U/S imaging and offers a multi-modality imaging environment for further target and anatomy delineation. This tool has been integrated into the visualization software "InViVo" which has been developed over several years in Fraunhofer IGD in Darmstadt.
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