Ventricle In Magnetic Resonance Images Research Articles

Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months

AbstractMagnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.

A Novel 3D-to-3D Diffeomorphic Registration Algorithm With Applications to Left Ventricle Segmentation in MR and Ultrasound Sequences

Left ventricular segmentation is a difficult and time-consuming task performed by clinicians, requiring the use of manual contours. We propose a novel three-dimensional diffeomorphic registration algorithm for endocardial segmentation of the left ventricle in magnetic resonance images and ultrasound temporal sequences. The proposed diffeomorphic registration method computes a voxel-to-voxel correspondence in three-dimensional space and is parameterized by one radial and three curl components to emulate the cardiac deformations. In addition, the proposed method allows for enforcing constraints to control the amount of deformation. The method was evaluated on 521 temporal frames from 20 patients from the Automated Cardiac Diagnosis Challenge magnetic resonance imaging dataset and 213 frames from 10 patients undergoing ultrasound scans from the Mazankowski Alberta Heart Institute. The algorithm was compared against six other registration methods, the Symmetric Normalization algorithm from the Dipy package, two variants of the Demons algorithm from the Insight Toolkit software package, two versions of RealTiTracker, and Elastix. The proposed method yielded overall Dice scores of 98.10 (0.90)% for the MRI dataset and 92.90 (2.42)% for the ultrasound dataset. The robustness of the algorithm is demonstrated by the high performance on multiple imaging modalities and various patient abnormalities.

Dawson's Fingers in Cerebral Small Vessel Disease.

To explore Dawson's fingers in cerebral small vessel disease (CSVD) and factors related to the development of Dawson's finger, we collected and analyzed clinical data of 65 patients with CSVD. We found a venous abnormality feature called Dawson's fingers around the ventricles in magnetic resonance images (MRIs) of 20 out of 65 patients with CSVD (30. 8%). A significant association between Dawson's fingers and diabetes mellitus (DM) was also detected (30 vs. 8.9%, P < 0.05). CSVD patients with Dawson's fingers had significantly increased cerebral microbleeds (CMB) (44.2 vs. 75.0%, p < 0.05), lacunae (66.7 vs. 95.0%, p < 0.05), and white matter hyperintensity (WMH) (p < 0.05) damage, and these patients exhibited significant cognitive domain impairment as assessed via Montreal Cognitive Assessment (MoCA) (18.9 ± 1.8 vs. 24.0 ± 0.8, p < 0.05) and Mini-Mental State Examination (MMSE) (24.5 ± 1.1 vs. 26.6 ± 0.6, p < 0.05). Our results show a distinctly high incidence of Dawson's fingers in CSVD patients and identify a significant association with DM, thus yielding insights about the appropriate use of Dawson's fingers, a venous imaging marker, to explore the basic pathophysiology of CSVD.

Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI

Segmentation of the left ventricle in magnetic resonance imaging (MRI) is important for assessing cardiac function. We present DT-GAN, a generative adversarial network (GAN) segmentation approach for the identification of the left ventricle in pediatric MRI. Segmentation of the left ventricle requires a large amount of annotated data; generating such data can be time-consuming and subject to observer variability. Additionally, it can be difficult to accomplish in a clinical setting. During the training of our GAN, we therefore introduce a semi-supervised semantic segmentation to reduce the number of images required for training, while maintaining a good segmentation accuracy. The GAN generator produces a segmentation label map and its discriminator outputs a confidence map, which gives the probability of a pixel coming from the label or from the generator. Moreover, we propose a new formulation of the GAN loss function based on distance transform and pixel-wise cross-entropy. This new loss function provides a better segmentation of boundary pixels, by favoring the correct classification of those pixels rather than focusing on pixels that are farther away from the boundary between anatomical structures. Our proposed method achieves a mean Hausdorff distance of 2.16 mm ± 0.42 mm (2.28 mm ± 0.21 mm for U-Net) and a Dice score of 0.88 ± 0.08 (0.91 ± 0.12 for U-Net) for the endocardium segmentation, using 50% of the annotated data. For the epicardium segmentation, we achieve a mean Hausdorff distance of 2.23 mm ± 0.35 mm (2.34 mm ± 0.39 mm for U-Net) and a Dice score of 0.93 mm ± 0.04 mm (0.89 ± 0.09 for U-Net). For the myocardium segmentation, we achieve a mean Hausdorff distance of 2.98 mm ± 0.43 mm (3.04 mm ± 0.27 mm for U-Net) and a Dice score of 0.79 mm ± 0.10 mm (0.74 ± 0.04 for U-Net). This new model could be very useful for the automatic analysis of cardiac MRI and for conducting large-scale studies based on MRI readings, with a limited amount of training data.

Automatic Localization and Segmentation of the Ventricles in Magnetic Resonance Images

Automatic localization and segmentation of the ventricles in magnetic resonance (MR) images is important for clinical applications. In this paper, we propose a new ventricle localization method based on slope difference distribution (SDD) clustering and threshold selection to calculate the positions of the left ventricle (LV) and the right ventricle (RV) automatically. Then, we utilize the SDD threshold selection to segment the LV and the RV in the automatically localized region of interest (ROI). To calculate the optimal threshold, we propose a coarse-to-fine SDD threshold selection method. Experimental results verified that both the proposed automatic ventricle localization method and the proposed coarse-to-fine SDD threshold selection method are more robust than state of the art methods.

Validation and development of a new automatic algorithm for time resolved segmentation of the left ventricle in magnetic resonance imaging

Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set , test set ). Manual delineation was reference standard and second observer analysis was performed in a subset (). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.

Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.

Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV −11 mL, ESV 1 mL, EF −3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.

Fourth Ventricle Metastasis of Colon Cancer: Case Report

Intracranial brain metastasis from colorectal cancers (CRC) is a rare occurrence in clinical practice. Moreover, metastasis to the ventricular system is even rarer. Here, we are reporting a case of CRC patient who had 4th ventricle metastasis without parenchymal metastasis. A 52-year-old male patient had a diagnosis of CRC 2 years earlier and received chemotherapy. He reported to an emergency department with abrupt vertigo, nausea and vomiting, and imbalanced walking. The lobulated mass was found in the 4th ventricle in magnetic resonance imaging. He was operated upon after aneurysm was excluded by MR angiography, and adenocarcinoma was found.

Optimizing the automatic segmentation of the left ventricle in magnetic resonance images

Automatic segmentation of the left ventricular (LV) myocardial borders in cardiovascular MR (CMR) images allows a significant speed-up of the procedure of quantifying LV function, and improves its reproducibility. The automated boundary delineation is usually based on a set of parameters that define the algorithms. Since the automatic segmentation algorithms are usually sensitive to the image quality and frequently depend heavily on the acquisition protocol, optimizing the parameters of the algorithm for such different protocols may be necessary to obtain optimal results. In other words, using a default set of parameters may be far from optimal for different scanners or protocols. For the MASS-software, for example, this means that a total of 14 parameters need to be optimized. This optimization is a difficult and labor-intensive process. To be able to more consistently and rapidly tune the parameters, an automated optimization system would be extremely desirable. In this paper we propose such an approach, which is based on genetic algorithms (GAs). The GA is an unsupervised iterative tool that generates new sets of parameters and converges toward an optimal set. We implemented and compared two different types of the genetic algorithms: a simple GA (SGA) and a steady state GA (2SGA). The difference between these two algorithms lies in the characteristics of the generated populations: "nonoverlapping populations" and "overlapping populations," respectively "nonoverlapping" population means that the two populations are disjoint, and "overlapping" means that the best parameters found in the previous generation are included in the present population. The performance of both algorithms was evaluated on twenty routinely obtained short-axis examinations (eleven examinations acquired with a steady-state free precession pulse sequence, and nine examinations with a gradient echo pulse sequence). The optimal parameters obtained with the GAs were used for the LV myocardial border delineation. Finally, the automatically outlined contours were compared to the gold standard--manually drawn contours by experts. The result of the comparison was expressed as a degree of similarity after a processing time of less than 72 h to a 59.5% of degree of similarity for SGA and a 66.7% of degree of similarity for 2SGA. In conclusion, genetic algorithms are very suitable to automatically tune the parameters of a border detection algorithm. Based on our data, the 2SGA was more suitable than the SGA method. This approach can be generalized to other optimization problems in medical image processing.