Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI &gt;0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Load-Independent And Regional Measures Of Cardiac Function Via Real-Time Mri

LOAD-INDEPENDENT AND REGIONAL MEASURES OF CARDIAC FUNCTION VIA REAL-TIME MRI Francisco Jose Contijoch Robert C Gorman, MD Expansion of infarcted tissue during left ventricular (LV) remodeling after a myocardial infarction is associated with poor long-term prognosis. Several interventions have been developed to limit infarct expansion by modifying the material properties of the infarcted or surrounding borderzone tissue. Measures of myocardial function and material properties can be obtained non-invasively via imaging. However, these measures are sensitive to variations in loading conditions and acquisition of load-independent measures have been limited by surgically invasive procedures and limited spatial resolution. In this dissertation, a real-time magnetic resonance imaging (MRI) technique was validated in clinical patients and instrumented animals, several technical improvements in MRI acquisition and reconstruction were presented for improved imaging resolution, load-independent measures were obtained in animal studies via non-invasive imaging, and regional variations in function were measured in both na�ve and post-infarction animals. Specifically, a golden-angle radial MRI acquisition with non-Cartesian SENSE-based reconstruction with an exposure time less than 95 ms and a frame rate above 89 fps allows for accurate estimation of LV slice volume in clinical patients and instrumented animals. Two technical developments were pursued to improve image quality and spatial resolution. First, the slice volume obtained can be used as a self-navigator signal to generate retrospectively-gated, high-resolution datasets of multiple beat morphologies. Second, cross-correlation of the ECG with previously observed values resulted in accurate interpretation of cardiac phase in patients with arrhythmias and allowed for multi-shot imaging of dynamic scenarios. Synchronizing the measured LV slice volume with an LV pressure signal allowed for pressure-volume loops and corresponding load-independent measures of function to be obtained in instrumented animals. Acquiring LV slice volume at multiple slice locations revealed regional differences in contractile function. Motion-tracking of the myocardium during real-time imaging allowed for differences in contractile function between normal, borderzone, and infarcted myocardium to be measured. Lastly, application of real-time imaging to patients with arrhythmias revealed the variable impact of ectopic beats on global hemodynamic function, depending on frequency and ectopic pattern. This work established the feasibility of obtaining load-independent measures of function via real-time MRI and illustrated regional variations in cardiac function

The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

The evaluation of left ventricular wall motion in Magnetic Resonance Imaging (MRI) clinical practice is based on a visual assessment of cine-MRI sequences. In fact, clinical interpreters (radiologists) proceed with a global visual evaluation of multiple cine-MRI sequences acquired in the three standard views. In addition, some functional parameters are quantified following a manual or a semi-automatic contouring of the myocardial borders. Although these parameters give information about the functional state of the left ventricle, they are not able to provide the location and the extent of wall motion abnormalities, which are associated with many cardiovascular diseases. In the past years, several approaches were developed to overcome the limitations of the classical evaluation techniques of left ventricular function. The aim of this article is to present an overview of the different methods and to summarize the relevant techniques based on myocardial contour detection and optical flow for regional assessment of left ventricular abnormalities

Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs