Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773±0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes

A novel model-based 3D+time left ventricular segmentation technique

A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D+time structures such as the cardiac left ventricle due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the ’curse of dimensionality’. Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epi-cardium contours of the left ventricle by coupling them at segmentationtime, instead of at model-time. We apply our approach to 33 3D+time MRI cardiac datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy

3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method

Objective Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new inter-modality SSM-based approach to detect the RV endocardium in CMR data. Methods Real-time transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semi-automatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with gold-standard (GS) corresponding parameters. Results CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2 min (including manual initialization) per patient. High correlations (r2 > 0.76) and not significant bias (Bland-Altman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87 ± 0.03), acceptable Hausdorff distance (9.35 ± 1.51 mm) and small point-to-surface distance (1.91 ± 0.26 mm). Conclusion A novel SSM-based approach to segment the RV endocardium in CMR scans by using a model trained on 3DE-derived RV endocardial surfaces, was proposed. This inter-modality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions

Medical image segmentation and analysis using statistical shape modelling and inter-landmark relationships

The study of anatomical morphology is of great importance to medical imaging, with applications varying from clinical diagnosis to computer-aided surgery. To this end, automated tools are required for accurate extraction of the anatomical boundaries from the image data and detailed interpretation of morphological information. This thesis introduces a novel approach to shape-based analysis of medical images based on Inter- Landmark Descriptors (ILDs). Unlike point coordinates that describe absolute position, these shape variables represent relative configuration of landmarks in the shape. The proposed work is motivated by the inherent difficulties of methods based on landmark coordinates in challenging applications. Through explicit invariance to pose parameters and decomposition of the global shape constraints, this work permits anatomical shape analysis that is resistant to image inhomogeneities and geometrical inconsistencies. Several algorithms are presented to tackle specific image segmentation and analysis problems, including automatic initialisation, optimal feature point search, outlier handling and dynamic abnormality localisation. Detailed validation results are provided based on various cardiovascular magnetic resonance datasets, showing increased robustness and accuracy.Open acces

Study on the Method of Constructing a Statistical Shape Model and Its Application to the Segmentation of Internal Organs in Medical Images

In image processing, segmentation is one of the critical tasks for diagnostic analysis and image interpretation. In the following thesis, we describe the investigation of three problems related to the segmentation algorithms for medical images: Active shape model algorithm, 3-dimensional (3-D) statistical shape model building and organic segmentation experiments. For the development of Active shape models, the constraints of statistical model reduced this algorithm to be difficult for various biological shapes. To overcome the coupling of parameters in the original algorithm, in this thesis, the genetic algorithm is introduced to relax the shape limitation. How to construct a robust and effective 3-D point model is still a key step in statistical shape models. Generally the shape information is obtained from manually segmented voxel data. In this thesis, a two-step procedure for generating these models was designed. After transformed the voxel data to triangular polygonal data, in the first step, attitudes of these interesting objects are aligned according their surface features. We propose to reflect the surface orientations by means of their Gauss maps. As well the Gauss maps are mapped to a complex plane using stereographic projection approach. The experiment was run to align a set of left lung models. The second step is identifying the positions of landmarks on polygonal surfaces. This is solved by surface parameterization method. We proposed two simplex methods to correspond the landmarks. A semi-automatic method attempts to “copy” the phasic positions of pre-placed landmarks to all the surfaces, which have been mapped to the same parameterization domain. Another automatic corresponding method attempts to place the landmarks equidistantly. Finally, the goodness experiments were performed to measure the difference to manually corresponded results. And we also compared the affection to correspondence when using different surface mapping methods. The third part of this thesis is applying the segmentation algorithms to solve clinical problems. We did not stick to the model-based methods but choose the suitable one or their complex according to the objects. In the experiment of lung regions segmentation which includes pulmonary nodules, we propose a complementary region growing method to deal with the unpredictable variation of image densities of lesion regions. In the experiments of liver regions, instead of using region growing method in 3-D style, we turn into a slice-by-slice style in order to reduce the overflows. The image intensity of cardiac regions is distinguishable from lung regions in CT image. But as to the adjacent zone of heart and liver boundary are generally blurry. We utilized a shape model guided method to refine the segmentation results.3-D segmentation techniques have been applied widely not only in medical imaging fields, but also in machine vision, computer graphic. At the last part of this thesis, we resume some interesting topics such as 3-D visualization for medical interpretation, human face recognition and object grasping robot etc.九州工業大学博士学位論文 学位記番号:工博甲第353号　学位授与年月日:平成25年9月27日Chapter 1: Introduction|Chapter 2: Framework of Medical Image Segmentation|Chapter 3: 2-D Organic Regions Using Active Shape Model and Genetic Algorithm|Chapter 4: Alignment of 3-D Models|Chapter 5: Corespondence of 3-D Models|Chapter 6:Experiments of Organic Segmentation|Chapter 7: Visualization Technology and Its Applications|Chapter 8: Conclusions and Future Works九州工業大学平成25年

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures

Computer-aided detection of wall motion abnormalities in cardiac MRI

With the increasing prevalence and hospitalization rate of ischaemic heart disease, an explosive growth of diagnostic imaging for ischaemia is ongoing. Clinical decision making on revascularization procedures requires reliable viability assessment to assure long-term patient survival and to elevate cost effectiveness of the therapy and treatment. As such, the demand is increasing for a computer-assisted diagnosis (CAD) method for ischaemic heart disease that supports clinicians with an objective analysis of infarct severity, a viability assessment or a prediction of potential functional improvement before performing revascularization. The goal of this thesis was to explore novel mechanisms that can be used for CAD in ischaemic heart disease, particularly through wall motion analysis from cardiac MR images. Existing diagnostic treatment of wall motion analysis from cardiac MR relies on visual wall motion scoring, which suffers from inter- and intra-observer variability. To minimize this variability, the automated method must contain essential knowledge on how the heart contracts normally. This enables automatic quantification of regional abnormal wall motion, detection of segments with contractile reserve and prediction of functional improvement in stress.1. Bontius Stichting inz. Doelfonds beeldverwerking, 2. Foundation Imago, 3. ASCI research school, and 4. Library of the University of Leiden.UBL - phd migration 201