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.
The detection and assessment of many hepatic diseases is based on examination of multiphase liver CT volumes.
Since phases contain complementary information, registration enables the radiologist to fuse the needed
information for diagnosis or operation planning. This work presents a novel multi-stage approach for automatic
registration of the liver in contrast enhanced CT volumes. Unlike other methods, our approach is based on automatic
pre-segmentation of the liver in the different phases. Using the resulting shape information the volumes
are coarsely registered using a landmark-based registration. Subsequently, deformations caused by the patient's
breathing are compensated by an elastic Demons algorithm with a boundary distance based speed function. This
allows for a high accuracy natural deformation without having to rely on error-prone extraction and matching
of the liver's internal structure in complementary phases. Furthermore, since shape information is given, surrounding
structures can be omitted which significantly speeds up registration. We evaluated our method using
22 CT volumes from 11 patients. The matching quality of outer shape and internal structures was validated by
radiology experts. The high quality results of our approach suggest its applicability in clinical practice.
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.
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.
This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed
Tomography (CT). The developed computer aided segmentation system is expected to support computer aided
diagnosis and operation planning. We have developed a deformable model based approach based on local shape
constraints that prevents the model from deforming into neighboring structures while allowing the global shape to
adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the
presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation
logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work
flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine
and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies,
the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation
results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared
to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation
times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for
an application in clinical practice.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.