A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

Automated Segmentation of Cerebellum Using Brain Mask and Partial Volume Estimation Map

While segmentation of the cerebellum is an indispensable step in many studies, its contrast is not clear because of the adjacent cerebrospinal fluid, meninges, and cerebra peduncle. Thus, various cerebellar segmentation methods, such as a deformable model or a template-based algorithm might exhibit incorrect segmentation of the venous sinuses and the cerebellar peduncle. In this study, we propose a fully automated procedure combining cerebellar tissue classification, a template-based approach, and morphological operations sequentially. The cerebellar region was defined approximately by removing the cerebral region from the brain mask. Then, the noncerebellar region was trimmed using a morphological operator and the brain-stem atlas was aligned to the individual brain to define the brain-stem area. The proposed method was validated with the well-known FreeSurfer and ITK-SNAP packages using the dice similarity index and recall and precision scores. As a result, the proposed method was significantly better than the other methods for the dice similarity index (0.93, FreeSurfer: 0.92, ITK-SNAP: 0.87) and precision (0.95, FreeSurfer: 0.90, ITK-SNAP: 0.93). Therefore, it could be said that the proposed method yielded a robust and accurate segmentation result. Moreover, additional postprocessing with the brain-stem atlas could improve its result.

MRI changes and complement activation correlate with epileptogenicity in a mouse model of temporal lobe epilepsy

The complex pathogenesis of temporal lobe epilepsy includes neuronal and glial pathology, synaptic reorganization, and an immune response. However, the spatio-temporal pattern of structural changes in the brain that provide a substrate for seizure generation and modulate the seizure phenotype is yet to be completely elucidated. We used quantitative magnetic resonance imaging (MRI) to study structural changes triggered by status epilepticus (SE) and their association with epileptogenesis and with activation of complement component 3 (C3). SE was induced by injection of pilocarpine in CD1 mice. Quantitative diffusion-weighted imaging and T2 relaxometry was performed using a 16.4-Tesla MRI scanner at 3 h and 1, 2, 7, 14, 28, 35, and 49 days post-SE. Following longitudinal MRI examinations, spontaneous recurrent seizures and interictal spikes were quantified using continuous video-EEG monitoring. Immunohistochemical analysis of C3 expression was performed at 48 h, 7 days, and 4 months post-SE. MRI changes were dynamic, reflecting different outcomes in relation to the development of epilepsy. Apparent diffusion coefficient changes in the hippocampus at 7 days post-SE correlated with the severity of the evolving epilepsy. C3 activation was found in all stages of epileptogenesis within the areas with significant MRI changes and correlated with the severity of epileptic condition.

An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior

PURPOSE

The objective of this paper was to automatically segment the cerebellum from T1-weighted human brain magnetic resonance (MR) images.

MATERIALS AND METHODS

The proposed method constructs a cerebellum template using five sets of 3-T MR imaging (MRI) data, which are used to determine the initial position and the shape prior of the cerebellum for the active contour model. Our formulation includes the active contour model with shape prior, which thereby maintains the shape of the template. The proposed active contour model is sequentially applied to sagittal-, coronal- and transverse-view images. To evaluate the proposed method, it is applied to BrainWeb data and a 3-T MRI data set and compared with FreeSurfer with respect to performance assessment metrics.

RESULTS

The segmented cerebellum was compared with the results from FreeSurfer. Using the manually segmented cerebellum as reference, we measured the average Jaccard coefficients of the proposed method, which were 0.882 and 0.885 for the BrainWeb data and 3-T MRI data set, respectively.

CONCLUSION

We presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. The proposed method yielded a robust and accurate segmentation result.

Automatic segmentation of magnetic resonance images using a decision tree with spatial information

Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data - phantom MR images obtained from IBSR (http://www.cma.mgh.harvard.edu/ibsr) and simulated brain MR images obtained from BrainWeb (http://www.bic.mni.mcgill.ca/brainweb) - were segmented using an automatic decision tree algorithm to obtain images with improved visual rendition. Spatial information on the general gray level (G), spatial gray level (S), and two-dimensional wavelet transform (W) was combined in-plane in two coordinate systems (Euclidean coordinates (x, y) or polar coordinates (r, theta)). The decision tree was constructed based on a binary tree with nodes created by splitting the distribution of input features of the tree. The spatial information obtained from MR images with different noise levels and inhomogeneities were segmented to compare whether the use of a decision tree improved the identification of human anatomical structures in a neuroimage. The average accuracy rates of segmentation for phantom images with a noise variation of 15 gray levels were 0.9999 and 0.9973 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The average accuracy rates of segmentation for simulated MR images with a noise level of 5% were 0.9532 and 0.9439 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The accuracy rates of segmentation were highest for both simulated phantom and brain MR images, having the lowest noise levels, from a reduction of overlapping gray levels in the images. The accuracies of segmentation were higher when the spatial information included the general gray level than when it included the spatial gray level, which in turn were higher than when it included the wavelet transform. Furthermore, the performance of segmentation was also evaluated with a boundary detection methodology that is based on the Hausdorff distance to compare with the mean computer to observer difference (COD) and mean interobserver difference (IOD) for gray matter (GM), white matter (WM), and all areas (ALL) from images segmented using the decision tree. The values of mean COD are similar and around 12mm for GM segmented using the decision tree. Our segmentation method based on a decision tree algorithm presented an easy way to perform automatic segmentation for both phantom and tissue regions in brain MR images.

Iterative active deformational methodology for tumor delineation: Evaluation across radiation treatment stage and volume

PURPOSE

To introduce, implement, and assess an iterative modification to the active deformational image segmentation method as applied to cervical cancer tumors.

MATERIALS AND METHODS

A comparison by Jaccard similarity (JS) between this active deformational method and manual segmentation was performed on tumors of various sizes across preradiation, 3 weeks postradiation, and 6 weeks postradiation using a General Linear Mixed Model across 121 studies from 52 patients with Stage IIB-IV cervical cancers.

RESULTS

The deformable segmentation method produced promising levels of agreement including JS factors of 0.71+/-0.11 in the preradiation studies. The analysis illustrated a rate of improvement in JS with increasing tumor volume that differed between the preradiation and 6 weeks postradiation stage (P=0.0474). In the large preradiated tumors each additional cm3 of volume was associated with an increase or improvement in JS of 0.0008 (95% confidence interval [CI]: 0.0003, 0.0014). In the smaller postradiation tumors, each additional cm3 of volume was associated with a more robust improvement in JS of 0.0046 (95% CI: 0.0009, 0.0082).

CONCLUSION

Agreement was strongly affected by tumor volume, and its performance was most impacted across volume in the later stages of radiation therapy. The deformation-based segmentation method appears to demonstrate utility for delineating cervical cancer tumors, particularly in the earliest stages of radiation treatment, where agreement is greatest.

Efficient and effective extraction of vocal fold vibratory patterns from high-speed digital imaging

High-speed digital imaging can provide valuable information on disordered voice production in voice science. However, the large amounts of high-speed image data with limited image resolutions produce significant challenges for computer analysis, and thus effective and efficient image edge extraction methods allowing for the batch analysis of high-speed images of vocal folds is clinically important. In this paper, a novel algorithm for automatic image edge detection is proposed to effectively and efficiently process high-speed images of the vocal folds. The method integrates Lagrange interpolation, differentiation, and Canny edge detection, which allow objective extraction of aperiodic vocal fold vibratory patterns from large numbers of high-speed digital images. This method and two other popular algorithms, histogram and active contour, are performed on 10 sets of high-speed video data from excised larynx experiments to compare their performances in analyzing high-speed images. The accuracy in computing glottal area and the computation time of these methods are investigated. The results show that our proposed method provides the most accurate and efficient detection, and is applicable when processing low-resolution images. In this study, we focus on developing a method to effectively and efficiently process high-speed image data from excised larynges. However, in addition we show the clinical potential of this method by use of example high-speed image data obtained from a patient with vocal nodules.The proposed automatic image-processing algorithm may provide a valuable biomedical application for the clinical assessment of vocal disorders by use of high-speed digital imaging.