Danilo Babin

Brain blood vessel segmentation using line-shaped profiles

Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

Generalized pixel profiling and comparative segmentation with application to arteriovenous malformation segmentation.

Extraction of structural and geometric information from 3-D images of blood vessels is a well known and widely addressed segmentation problem. The segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, with a special application in diagnostics and surgery on arteriovenous malformations (AVM). However, the techniques addressing the problem of the AVM inner structure segmentation are rare. In this work we present a novel method of pixel profiling with the application to segmentation of the 3-D angiography AVM images. Our algorithm stands out in situations with low resolution images and high variability of pixel intensity. Another advantage of our method is that the parameters are set automatically, which yields little manual user intervention. The results on phantoms and real data demonstrate its effectiveness and potentials for fine delineation of AVM structure.

MR pulse wave velocity increases with age faster in the thoracic aorta than in the abdominal aorta

To assess the difference between thoracic and abdominal aortic pulse wave velocity (PWV) in apparently healthy subjects including young adults to elderly subjects.

Segmentation and length measurement of the abdominal blood vessels in 3-D MRI images

In diagnosing diseases and planning surgeries the structure and length of blood vessels is of great importance. In this research we develop a novel method for the segmentation of 2-D and 3-D images with an application to blood vessel length measurements in 3-D abdominal MRI images. Our approach is robust to noise and does not require contrast-enhanced images for segmentation. We use an effective algorithm for skeletonization, graph construction and shortest path estimation to measure the length of blood vessels of interest.

Robust segmentation methods with an application to aortic pulse wave velocity calculation

Aortic stiffness has proven to be an important diagnostic and prognostic factor of many cardiovascular diseases, as well as an estimate of overall cardiovascular health. Pulse wave velocity (PWV) represents a good measure of the aortic stiffness, while the aortic distensibility is used as an aortic elasticity index. Obtaining the PWV and the aortic distensibility from magnetic resonance imaging (MRI) data requires diverse segmentation tasks, namely the extraction of the aortic center line and the segmentation of aortic regions, combined with signal processing methods for the analysis of the pulse wave. In our study non-contrasted MRI images of abdomen were used in healthy volunteers (22 data sets) for the sake of non-invasive analysis and contrasted magnetic resonance (MR) images were used for the aortic examination of Marfan syndrome patients (8 data sets). In this research we present a novel robust segmentation technique for the PWV and aortic distensibility calculation as a complete image processing toolbox. We introduce a novel graph-based method for the centerline extraction of a thoraco-abdominal aorta for the length calculation from 3-D MRI data, robust to artifacts and noise. Moreover, we design a new projection-based segmentation method for transverse aortic region delineation in cardiac magnetic resonance (CMR) images which is robust to high presence of artifacts. Finally, we propose a novel method for analysis of velocity curves in order to obtain pulse wave propagation times. In order to validate the proposed method we compare the obtained results with manually determined aortic centerlines and a region segmentation by an expert, while the results of the PWV measurement were compared to a validated software (LUMC, Leiden, the Netherlands). The obtained results show high correctness and effectiveness of our method for the aortic PWV and distensibility calculation.

Segmentation of brain blood vessels using projections in 3-D CT angiography images

Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user intervention. In this work we propose an automatic algorithm for efficient segmentation of 3-D CT angiography images of cerebral blood vessels. Our method is robust to high intensity noise and is able to accurately segment blood vessels with high range of luminance values, as well as low-contrast vessels.

Segmentation of airways in lungs using projections in 3-D CT angiography images

In diagnosing lung diseases, the structure and shape of airways in lungs are of great importance. In this paper we propose a novel method for segmenting low-contrast 3-D CTA images of airways in lungs. Our approach is an edge-detecting slice-by-slice segmentation method, capable of segmenting low contrasted airway regions. Our segmentation using projections method shows robustness in images with high presence of noise.

Proximal aortic stiffening in Turner patients may be present before dilation can be detected: a segmental functional MRI study

BackgroundTo study segmental structural and functional aortic properties in Turner syndrome (TS) patients. Aortic abnormalities contribute to increased morbidity and mortality of women with Turner syndrome. Cardiovascular magnetic resonance (CMR) allows segmental study of aortic elastic properties.MethodWe performed Pulse Wave Velocity (PWV) and distensibility measurements using CMR of the thoracic and abdominal aorta in 55 TS-patients, aged 13-59y, and in a control population (n = 38;12-58y). We investigated the contribution of TS on aortic stiffness in our entire cohort, in bicuspid (BAV) versus tricuspid (TAV) aortic valve-morphology subgroups, and in the younger and older subgroups.ResultsDifferences in aortic properties were only seen at the most proximal aortic level. BAV Turner patients had significantly higher PWV, compared to TAV Turner (p = 0.014), who in turn had significantly higher PWV compared to controls (p = 0.010). BAV Turner patients had significantly larger ascending aortic (AA) luminal area and lower AA distensibility compared to both controls (all p < 0.01) and TAV Turner patients. TAV Turner had similar AA luminal areas and AA distensibility compared to Controls. Functional changes are present in younger and older Turner subjects, whereas ascending aortic dilation is prominent in older Turner patients. Clinically relevant dilatation (TAV and BAV) was associated with reduced distensibility.ConclusionAortic stiffening and dilation in TS affects the proximal aorta, and is more pronounced, although not exclusively, in BAV TS patients.Functional abnormalities are present at an early age, suggesting an aortic wall disease inherent to the TS. Whether this increased stiffness at young age can predict later dilatation needs to be studied longitudinally.

Pixel profiling for extraction of arteriovenous malformation in 3-D CTA images

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture which can cause severe brain damage or even death. For planing the embolization procedure of an AVM, the knowledge of the accurate location and size of the malformation is of utmost importance. The main purposes of automatic AVM segmentation are: 1) objective and reproducible segmentation; 2) reduction in processing time (saving resources by requiring less manual work). Furthermore, automatic segmentation with accurate AVM (or aneurysm) characterization were deemed helpful in therapeutic decision making concerning treatment modality (surgical or endovascular). Operator-independent accurate sizing of AVM (aneurysm) would allow strict follow-up until the threshold is reached and the patient referred to treatment. We propose in this paper a novel AVM detection method and a blood vessel tree analysis approach using ordered thinning-based skeletonization. The main contributions are: (1) a new method of profile volume calculation to replace the distance labels in ordered skeletonization; (2) an automatic method for AVM detection and extraction, with accurate positioning and malformation size estimation. The main idea in our work is use the structural (anatomical) vessel differences and the inhomogeneities in distribution of pixel gray values to locate and extract the AVM. The algorithm takes a segmentation result as an input to perform AVM delineation. The algorithm determines the AVM region automatically, without any user interaction and independently of the segmentation algorithm used. The proposed approach is validated on brain blood vessel CTA images before and after embolization. The results obtained using the Dice coefficient comparisons, the volume percent error and the AVM center position show high accuracy of our method and indicate potentials for use in surgical planning.

Skeleton calculation for automatic extraction of arteriovenous malformation in 3-D CTA images

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture which can cause severe brain damage or even death. For planing of embolization procedure of an AVM, the accurate knowledge of the location and size of the malformation is of utmost importance. We propose in this paper a novel AVM delineation approach using ordered thinning-based skeletonization. The main contribution is a new method for creating the graph-type skeleton from the result of the ordered skeletonization, and an automatic method for AVM detection and extraction. The main idea in our work is to use the structural (anatomical) vessel differences and the inhomogeneities in distribution of pixel gray values to locate and extract the AVM. The algorithm takes the segmentation result as an input to perform AVM delineation. It determines the AVM region automatically, without any user interaction, independently of the used segmentation algorithm. The proposed approach is validated on brain blood vessel CTA images before and after embolization. The results obtained using the Dice coefficient comparisons, the volume relative error and the AVM center position show high accuracy of our method and indicate potentials for use in surgical planning.