Simultaneous Analysis of 2D Echo Views for Left Atrial Segmentation and Disease Detection.

We propose a joint information approach for automatic analysis of 2D echocardiography (echo) data. The approach combines a priori images, their segmentations and patient diagnostic information within a unified framework to determine various clinical parameters, such as cardiac chamber volumes, and cardiac disease labels. The main idea behind the approach is to employ joint Independent Component Analysis of both echo image intensity information and corresponding segmentation labels to generate models that jointly describe the image and label space of echo patients on multiple apical views, instead of independently. These models are then both used for segmentation and volume estimation of cardiac chambers such as the left atrium and for detecting pathological abnormalities such as mitral regurgitation. We validate the approach on a large cohort of echoes obtained from 6,993 studies. We report performance of the proposed approach in estimation of the left-atrium volume and detection of mitral-regurgitation severity. A correlation coefficient of 0.87 was achieved for volume estimation of the left atrium when compared to the clinical report. Moreover, we classified patients that suffer from moderate or severe mitral regurgitation with an average accuracy of 82%.

Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View.

Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert's manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Learning-Based Multi-Label Segmentation of Transrectal Ultrasound Images for Prostate Brachytherapy.

Low-dose-rate prostate brachytherapy treatment takes place by implantation of small radioactive seeds in and sometimes adjacent to the prostate gland. A patient specific target anatomy for seed placement is usually determined by contouring a set of collected transrectal ultrasound images prior to implantation. Standard-of-care in prostate brachytherapy is to delineate the clinical target anatomy, which closely follows the real prostate boundary. Subsequently, the boundary is dilated with respect to the clinical guidelines to determine a planning target volume. Manual contouring of these two anatomical targets is a tedious task with relatively high observer variability. In this work, we aim to reduce the segmentation variability and planning time by proposing an efficient learning-based multi-label segmentation algorithm. We incorporate a sparse representation approach in our methodology to learn a dictionary of sparse joint elements consisting of images, and clinical and planning target volume segmentation. The generated dictionary inherently captures the relationships among elements, which also incorporates the institutional clinical guidelines. The proposed multi-label segmentation method is evaluated on a dataset of 590 brachytherapy treatment records by 5-fold cross validation. We show clinically acceptable instantaneous segmentation results for both target volumes.

Ultrasound-Guided Spine Anesthesia: Feasibility Study of a Guidance System.

Spinal needle injections are guided by fluoroscopy or palpation, resulting in radiation exposure and/or multiple needle re-insertions. Consequently, guiding these procedures with live ultrasound has become more popular, but images are still challenging to interpret. We introduce a guidance system based on augmentation of ultrasound images with a patient-specific 3-D surface model of the lumbar spine. We assessed the feasibility of the system in a study on 12 patients. The system could accurately provide augmentations of the epidural space and the facet joint for all subjects. Following conventional, fluoroscopy-guided needle placement, augmentation accuracy was determined according to the electromagnetically tracked final position of the needle. In 9 of 12 cases, the accuracy was considered sufficient for successfully delivering anesthesia. The unsuccessful cases can be attributed to errors in the electromagnetic tracking reference, which can be avoided by a setup reducing the influence of the metal C-arm.

A multi-atlas-based segmentation framework for prostate brachytherapy.

Low-dose-rate brachytherapy is a radiation treatment method for localized prostate cancer. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate in order to devise a plan to deliver sufficient radiation dose to the cancerous tissue. Brachytherapy planning involves delineation of contours in these images, which closely follow the prostate boundary, i.e., clinical target volume. This process is currently performed either manually or semi-automatically, which requires user interaction for landmark initialization. In this paper, we propose a multi-atlas fusion framework to automatically delineate the clinical target volume in ultrasound images. A dataset of a priori segmented ultrasound images, i.e., atlases, is registered to a target image. We introduce a pairwise atlas agreement factor that combines an image-similarity metric and similarity between a priori segmented contours. This factor is used in an atlas selection algorithm to prune the dataset before combining the atlas contours to produce a consensus segmentation. We evaluate the proposed segmentation approach on a set of 280 transrectal prostate volume studies. The proposed method produces segmentation results that are within the range of observer variability when compared to a semi-automatic segmentation technique that is routinely used in our cancer clinic.

Ultrasound-guided spinal injections: a feasibility study of a guidance system.

PURPOSE: Facet joint injections of analgesic agents are widely used to treat patients with lower back pain. The current standard-of-care for guiding the injection is fluoroscopy, which exposes the patient and physician to significant radiation. As an alternative, several ultrasound guidance systems have been proposed, but have not become the standard-of-care, mainly because of the difficulty in image interpretation by the anesthesiologist unfamiliar with the complex spinal sonography. METHODS: We introduce an ultrasound-based navigation system that allows for live 2D ultrasound images augmented with a patient-specific statistical model of the spine and relating this information to the position of the tracked injection needle. The model registration accuracy is assessed on ultrasound data obtained from nine subjects who had prior CT images as the gold standard for the statistical model. The clinical validity of our method is evaluated on four subjects (of an ongoing in vivo study) which underwent facet joint injections. RESULTS: The statistical model could be registered to the bone structures in the ultrasound volume with an average RMS accuracy of 2.3±0.4 mm. The shape of the individual vertebrae could be estimated from the US volume with an average RMS surface distance error of 1.5±0.4 mm. The facet joints could be identified by the statistical model with an average accuracy of 5.1 ± 1.5 mm. CONCLUSIONS: The results of this initial feasibility assessment suggest that this ultrasound-based system is capable of providing information sufficient to guide facet joint injections. Further clinical studies are warranted.

Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions.

A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.

Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.

OBJECTIVE: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. METHODS: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. RESULTS: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. CONCLUSION: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. SIGNIFICANCE: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

An automatic multi-atlas segmentation of the prostate in transrectal ultrasound images using pairwise atlas shape similarity.

Delineation of the prostate from transrectal ultrasound images is a necessary step in several computer-assisted clinical interventions, such as low dose rate brachytherapy. Current approaches to user segmentation require user intervention and therefore it is subject to user errors. It is desirable to have a fully automatic segmentation for improved segmentation consistency and speed. In this paper, we propose a multi-atlas fusion framework to automatically segment prostate transrectal ultrasound images. The framework initially registers a dataset of a priori segmented ultrasound images to a target image. Subsequently, it uses the pairwise similarity of registered prostate shapes, which is independent of the image-similarity metric optimized during the registration process, to prune the dataset prior to the fusion and consensus segmentation step. A leave-one-out cross-validation of the proposed framework on a dataset of 50 transrectal ultrasound volumes obtained from patients undergoing brachytherapy treatment shows that the proposed is clinically robust, accurate and reproducible.