Automated segmentation on the entire cardiac cycle using a deep learning work-flow

The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-automated LV segmentation that learns from images acquired through the cardiac cycle. The workflow consists of three components: first, for each image in the sequence, we perform an automated localization and subsequent cropping of the bounding box containing the cardiac silhouette. Second, we identify the LV contours using a Temporal Fully Convolutional Neural Network (T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a recurrent mechanism enforcing temporal coherence across consecutive frames. Finally, we further defined the boundaries using either one of two components: fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials and Semantic Flow. Our initial experiments suggest that significant improvement in performance can potentially be achieved by using a recurrent neural network component that explicitly learns cardiac motion patterns whilst performing LV segmentation.Comment: 6 pages, 2 figures, published on IEEE Xplor

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

MRI evidence for altered venous drainage and intracranial compliance in mild traumatic brain injury.

To compare venous drainage patterns and associated intracranial hydrodynamics between subjects who experienced mild traumatic brain injury (mTBI) and age- and gender-matched controls. Thirty adult subjects (15 with mTBI and 15 age- and gender-matched controls) were investigated using a 3T MR scanner. Time since trauma was 0.5 to 29 years (mean 11.4 years). A 2D-time-of-flight MR-venography of the upper neck was performed to visualize the cervical venous vasculature. Cerebral venous drainage through primary and secondary channels, and intracranial compliance index and pressure were derived using cine-phase contrast imaging of the cerebral arterial inflow, venous outflow, and the craniospinal CSF flow. The intracranial compliance index is the defined as the ratio of maximal intracranial volume and pressure changes during the cardiac cycle. MR estimated ICP was then obtained through the inverse relationship between compliance and ICP. Compared to the controls, subjects with mTBI demonstrated a significantly smaller percentage of venous outflow through internal jugular veins (60.9±21% vs. controls: 76.8±10%; p = 0.01) compensated by an increased drainage through secondary veins (12.3±10.9% vs. 5.5±3.3%; p<0.03). Mean intracranial compliance index was significantly lower in the mTBI cohort (5.8±1.4 vs. controls 8.4±1.9; p<0.0007). Consequently, MR estimate of intracranial pressure was significantly higher in the mTBI cohort (12.5±2.9 mmHg vs. 8.8±2.0 mmHg; p<0.0007). mTBI is associated with increased venous drainage through secondary pathways. This reflects higher outflow impedance, which may explain the finding of reduced intracranial compliance. These results suggest that hemodynamic and hydrodynamic changes following mTBI persist even in the absence of clinical symptoms and abnormal findings in conventional MR imaging

Unsupervised Myocardial Segmentation for Cardiac BOLD

A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace.Our method is based on variational pre-processing and spatial regularization using Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns