Ultrasound (US) is the modality of choice for fetal screening, which includes the assessment of a variety of standardized growth measurements, like the abdominal circumference (AC). Screening guidelines define criteria on the scan plane, in which the measurement is taken. As US is increasingly becoming a 3D modality, approaches for automated determination of the optimal scan plane in a volumetric dataset would greatly improve the workflow. In this work, a novel framework for deep hyperplane learning is proposed and applied for view plane estimation in fetal US examinations. The approach is tightly integrated in the clinical workflow and consists of two main steps. First, the bounding box around the structure of interest is determined in the central slice (MPR). Second, offsets from the structure in the bounding box to the optimal view plane are estimated. By linear regression through the estimated offsets, the view plane coordinates can then be determined. The presented approach is successfully applied on clinical screening data for AC plane estimation and a high accuracy is obtained, outperforming or comparable to recent publications on the same application.
Ultrasound is increasingly becoming a 3D modality. Mechanical and matrix array transducers are able to deliver 3D images with good spatial and temporal resolution. The 3D imaging facilitates the application of automated image analysis to enhance workflows, which has the potential to make ultrasound a less operator dependent modality. However, the analysis of the more complex 3D images and definition of all examination standards on 2D images pose barriers to the use of 3D in daily clinical practice. In this paper, we address a part of the canonical fetal screening program, namely the localization of the abdominal cross-sectional plane with the corresponding measurement of the abdominal circumference in this plane. For this purpose, a fully automated pipeline has been designed starting with a random forest based anatomical landmark detection. A feature trained shape model of the fetal torso including inner organs with the abdominal cross-sectional plane encoded into the model is then transformed into the patient space using the landmark localizations. In a free-form deformation step, the model is individualized to the image, using a torso probability map generated by a convolutional neural network as an additional feature image. After adaptation, the abdominal plane and the abdominal torso contour in that plane are directly obtained. This allows the measurement of the abdominal circumference as well as the rendering of the plane for visual assessment. The method has been trained on 126 and evaluated on 42 abdominal 3D US datasets. An average plane offset error of 5.8 mm and an average relative circumference error of 4.9 % in the evaluation set could be achieved.
The diagnosis of cardiac function based on cine MRI requires the segmentation of cardiac structures in the images, but the problem of automatic cardiac segmentation is still open, due to the imaging characteristics of cardiac MR images and the anatomical variability of the heart. In this paper, we present a variational framework for joint segmentation and registration of multiple structures of the heart. To enable the simultaneous segmentation and registration of multiple objects, a shape prior term is introduced into a region competition approach for multi-object level set segmentation. The proposed algorithm is applied for simultaneous segmentation of the myocardium as well as the left and right ventricular blood pool in short axis cine MRI images. Two experiments are performed: first, intra-patient 4D segmentation with a given initial segmentation for one time-point in a 4D sequence, and second, a multi-atlas segmentation strategy is applied to unseen patient data. Evaluation of segmentation accuracy is done by overlap coefficients and surface distances. An evaluation based on clinical 4D cine MRI images of 25 patients shows the benefit of the combined approach compared to sole registration and sole segmentation.
4D imaging becomes increasingly important in clinical practice. Its use in diagnostics and therapy planning usually requires the application of non-linear registration techniques. The reliability of information derived from the computed transformations is directly dependent on the registration accuracy. Ideally, this accuracy should be evaluated on a patient- and data-specific level { which requires appropriate evaluation criteria and procedures. A standard approach for evaluation of non-linear registration accuracy is to compute a landmark- or point-based registration error by means of manually detected landmark correspondences in the images to register, with the landmarks being anatomically characteristic points. Manual detection of such points is, however, time-consuming and error-prone. In this contribution, different operators for automatic landmark detection and a block matching strategy for landmark propagation in 4D image sequences (here: 4D lung CT, 4D liver MRT) are proposed and evaluated. It turns out that the so-called Förstner-Rohr operators perform best for detection of anatomically characteristic points and that the proposed propagation strategy ensures a robust transfer of these landmarks between the images. The automatically detected landmark correspondences are then used to evaluate the accuracy of different registration approaches (in total 48 variants) applied for registering 4D lung CT data. The resulting registration error values are compared to errors obtained by manually detected landmark pairs. It is shown that derived statements concerning differences in accuracy of the registration approaches are identical for both the manually and the automatically detected landmark sets.
KEYWORDS: Motion estimation, Principal component analysis, Radiotherapy, Tumors, Lung, Motion models, Motion measurement, 4D CT imaging, Chest, Simulation of CCA and DLA aggregates
Respiratory motion is a major source of error in radiation treatment of thoracic and abdominal tumors. State-of-the-art motion-adaptive radiation therapy techniques are usually guided by external breathing signals acting
as surrogates for the internal motion of organs and tumors. Assuming a relationship between the surrogate
measurements and the internal motion patterns, which are usually described by non-linear transformations,
correspondence models can be defined and used for surrogate-based motion estimation. In this contribution,
a diffeomorphic motion estimation framework based on standard multivariate linear regression is extended by
subspace-based approaches like principal component analysis, partial least squares, and canonical correlation
analysis. These methods aim at exploiting the hidden structure of the training data to improve the use of
the information provided by high-dimensional surrogate and internal motion representations. A quantitative
evaluation carried out on 4D CT data sets of 10 lung tumor patients shows that subspace-based approaches
are able to significantly improve the mean estimation accuracy when compared to standard multivariate linear
regression.
Although 4D CT imaging becomes available in an increasing number of radiotherapy facilities, 3D imaging and
planning is still standard in current clinical practice. In particular for lung tumors, respiratory motion is a
known source of uncertainty and should be accounted for during radiotherapy planning - which is difficult by
using only a 3D planning CT. In this contribution, we propose applying a statistical lung motion model to
predict patients' motion patterns and to estimate dosimetric motion effects in lung tumor radiotherapy if only
3D images are available. Being generated based on 4D CT images of patients with unimpaired lung motion, the
model tends to overestimate lung tumor motion. It therefore promises conservative risk assessment regarding
tumor dose coverage. This is exemplarily evaluated using treatment plans of lung tumor patients with different
tumor motion patterns and for two treatment modalities (conventional 3D conformal radiotherapy and step-&-
shoot intensity modulated radiotherapy). For the test cases, 4D CT images are available. Thus, also a standard
registration-based 4D dose calculation is performed, which serves as reference to judge plausibility of the modelbased
4D dose calculation. It will be shown that, if combined with an additional simple patient-specific breathing
surrogate measurement (here: spirometry), the model-based dose calculation provides reasonable risk assessment
of respiratory motion effects.
Automatic segmentation of the separate human lung lobes is a crucial task in computer aided diagnostics and
intervention planning, and required for example for determination of disease spreading or pulmonary parenchyma
quantification.
In this work, a novel approach for lobe segmentation based on multi-region level sets is presented. In a first step,
interlobular fissures are detected using a supervised enhancement filter. The fissures are then used to compute
a cost image, which is incorporated in the level set approach. By this, the segmentation is drawn to the fissures
at places where structure information is present in the image. In areas with incomplete fissures (e.g. due to
insufficient image quality or anatomical conditions) the smoothing term of the level sets applies and a closed
continuation of the fissures is provided.
The approach is tested on nine pulmonary CT scans. It is shown that incorporating the additional force term
improves the segmentation significantly. On average, 83% of the left fissure is traced correctly; the right oblique
and horizontal fissures are properly segmented to 76% and 48%, respectively.
Exact cerebrovascular segmentations based on high resolution 3D anatomical datasets are required for many
clinical applications. A general problem of most vessel segmentation methods is the insufficient delineation
of small vessels, which are often represented by rather low intensities and high surface curvatures. This paper
describes an improved direction-dependent level set approach for the cerebrovascular segmentation. The proposed
method utilizes the direction information of the eigenvectors computed by vesselness filters for adjusting the
weights of the internal energy depending on the location. The basic idea of this is to weight the internal energy
lower in case the gradient of the level set is comparable to the direction of the eigenvector extracted by the
vesselness filter. A quantitative evaluation of the proposed method based on three clinical Time-of-Flight MRA
datasets with available manual segmentations using the Tanimoto coefficient showed that a mean improvement
compared to the initial segmentation of 0.081 is achieved, while the corresponding level set segmentation without
integration of direction information does not lead to satisfying results. In summary, the proposed method enables
an improved delineation of small vessels, especially of those represented by low intensities and high surface
curvatures.
Image registration is one of the most common research areas in medical image processing. It is required for
example for image fusion, motion estimation, patient positioning, or generation of medical atlases.
In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter
indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple
factors like the application and the occurrence of noise in the images, and may therefore vary from case to
case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further
adjustment of the parameters.
In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear
intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the
images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for
quantifying registration accuracy, is then used to optimize the parameter choice during the registration process.
The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show
that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.
The estimation of respiratory motion is a fundamental requisite for many applications in the field of 4D medical
imaging, for example for radiotherapy of thoracic and abdominal tumors. It is usually done using non-linear
registration of time frames of the sequence without further modelling of physiological motion properties. In this
context, the accurate calculation of liver und lung motion is especially challenging because the organs are slipping
along the surrounding tissue (i.e. the rib cage) during the respiratory cycle, which leads to discontinuities in the
motion field. Without incorporating this specific physiological characteristic, common smoothing mechanisms
cause an incorrect estimation along the object borders.
In this paper, we present an extended diffusion-based model for incorporating physiological knowledge in image
registration. By decoupling normal- and tangential-directed smoothing, we are able to estimate slipping motion
at the organ borders while preventing gaps and ensuring smooth motion fields inside.
We evaluate our model for the estimation of lung and liver motion on the basis of publicly accessible 4D CT
and 4D MRI data. The results show a considerable increase of registration accuracy with respect to the target
registration error and a more plausible motion estimation.
Exact segmentations of the cerebrovascular system are the basis for several medical applications, like preoperation
planning, postoperative monitoring and medical research. Several automatic methods for the extraction of the
vascular system have been proposed. These automatic approaches suffer from several problems. One of the
major problems are interruptions in the vascular segmentation, especially in case of small vessels represented by
low intensities. These breaks are problematic for the outcome of several applications e.g. FEM-simulations and
quantitative vessel analysis. In this paper we propose an automatic post-processing method to connect broken
vessel segmentations. The approach proposed consists of four steps. Based on an existing vessel segmentation
the 3D-skeleton is computed first and used to detect the dead ends of the segmentation. In a following step
possible connections between these dead ends are computed using a graph based approach based on the vesselness
parameter image. After a consistency check is performed, the detected paths are used to obtain the final
segmentation using a level set approach. The method proposed was validated using a synthetic dataset as well as
two clinical datasets. The evaluation of the results yielded by the method proposed based on two Time-of-Flight
MRA datasets showed that in mean 45 connections between dead ends per dataset were found. A quantitative
comparison with semi-automatic segmentations by medical experts using the Dice coefficient revealed that a
mean improvement of 0.0229 per dataset was achieved. In summary the approach presented can considerably
improve the accuracy of vascular segmentations needed for following analysis steps.
In this article, we propose a unified statistical framework for image segmentation with shape prior information.
The approach combines an explicitely parameterized point-based probabilistic statistical shape model (SSM)
with a segmentation contour which is implicitly represented by the zero level set of a higher dimensional surface.
These two aspects are unified in a Maximum a Posteriori (MAP) estimation where the level set is evolved to
converge towards the boundary of the organ to be segmented based on the image information while taking into
account the prior given by the SSM information. The optimization of the energy functional obtained by the MAP
formulation leads to an alternate update of the level set and an update of the fitting of the SSM. We then adapt
the probabilistic SSM for multi-shape modeling and extend the approach to multiple-structure segmentation by
introducing a level set function for each structure. During segmentation, the evolution of the different level set
functions is coupled by the multi-shape SSM. First experimental evaluations indicate that our method is well
suited for the segmentation of topologically complex, non spheric and multiple-structure shapes. We demonstrate
the effectiveness of the method by experiments on kidney segmentation as well as on hip joint segmentation in
CT images.
We propose a method to compute a 4D statistical model of respiratory lung motion which consists of a 3D shape
atlas, a 4D mean motion model and a 4D motion variability model. Symmetric diffeomorphic image registration
is used to estimate subject-specific motion models, to generate an average shape and intensity atlas of the lung
as anatomical reference frame and to establish inter-subject correspondence. The Log-Euclidean framework
allows to perform statistics on diffeomorphic transformations via vectorial statistics on their logarithms. We
apply this framework to compute the mean motion and motion variations by performing a Principal Component
Analysis (PCA) on diffeomorphisms. Furthermore, we present methods to adapt the generated statistical 4D
motion model to a patient-specific lung geometry and the individual organ motion.
The prediction performance is evaluated with respect to motion field differences and with respect to landmark-
based target registration errors. The quantitative analysis results in a mean target registration error of 3,2 ± 1,8
mm. The results show that the new method is able to provide valuable knowledge in many fields of application.
Spatiotemporal image data allow analyzing respiratory dynamics and its impact on radiation therapy. A key
feature within this field of research is the process of lung motion field estimation. For a multitude of applications
feasible and "realistic" motion field estimates are required. Widely non-linear registration methods are applied
to estimate motion fields; in this case physiology is not taken into account. Using Finite Element Methods we
implemented a biophysical approach to model respiratory lung motion starting with the physiology of breathing.
Resulting motion models are compared to motion field estimates of a non-linear non-parametric intensity-based
registration approach. Additionally, we extended the registration approach to cope with discontinuities in pleura
and chest wall motion as motivated by the biophysical model. Accuracy of the different modeling approaches is
evaluated using a total of 800 user-defined landmarks in 4D(=3D+t) CT data of 10 lung tumor patients (between
70 and 90 landmarks each patient). Mean registration residuals (= difference between landmark motion as predicted
model-based and as observed by an expert) are 3.2±2.0 mm (biophysical model), 3.4±2.4 mm (registration
of segmented lung data), 2.1±2.3 mm (registration of CT data), and 1.6±1.3 mm (extended registration of CT
data); intraobserver variability of landmark identification is 0.9±0.8 mm, mean landmark motion is 6.8±5.4 mm.
Thus, prediction accuracy is higher for non-linear registration of the CT data, but it is shown that explicit modeling
of boundary conditions motivated by the physiology of breathing and the biophysical modeling approach,
respectively, improves registration accuracy significantly.
Spatiotemporal image data sets, like 4D CT or dynamic MRI, open up the possibility to estimate respiratory
induced tumor and organ motion and to generate four-dimensional models that describe the temporal change in
position and shape of structures of interest. However, two main problems arise: the structures of interest have to
be segmented in the 4D data set and and the organ motion has to be estimated in the temporal image sequence.
This paper presents a variational approach for simultaneous segmentation and registration applied to temporal
image sequences. The proposed method assumes a known segmentation in one frame and then recovers nonlinear
registration and segmentation in other frames by minimizing a cost function that combines intensity-based
registration, level-set segmentation as well as prior shape and intensity knowledge. The purpose of the presented
method is to estimate respiration induced organ motion in spatiotemporal CT image sequences and to segment
a structure of interest simultaneously.
A validation of the combined registration and segmentation approach is presented using low dose 4D CT
data sets of the liver. The results demonstrate that the simultaneous solution of both problems improves the
segmentation performance over a sequential application of the registration and segmentation steps.
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