Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separatel

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods

Spatio-Temporal Modelling of Perfusion Cardiovascular MRI

Myocardial perfusion MRI provides valuable insight into how coronary artery and microvascular diseases affect myocardial tissue. Stenosis in a coronary vessel leads to reduced maximum blood flow (MBF), but collaterals may secure the blood supply of the myocardium but with altered tracer kinetics. To date, quantitative analysis of myocardial perfusion MRI has only been performed on a local level, largely ignoring the contextual information inherent in different myocardial segments. This paper proposes to quantify the spatial dependencies between the local kinetics via a Hierarchical Bayesian Model (HBM). In the proposed framework, all local systems are modelled simultaneously along with their dependencies, thus allowing more robust context-driven estimation of local kinetics. Detailed validation on both simulated and patient data is provided

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentation

Generalized Zero Shot Learning For Medical Image Classification

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of different disease classes; and 2) training a feature generator. Our approach does not require class attribute vectors which are available for natural images but not for medical images. SSL ensures that the anchor vectors are representative of each class. SSL is also used to generate synthetic features of unseen classes. Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images. Our method is adaptable enough to accommodate class attribute vectors when they are available for natural images