Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

Practical Priors for Bayesian Inference of Latent Biomarkers

Latent biomarkers are quantities that strongly relate to patient's disease diagnosis and prognosis, but are difficult to measure or even not directly observable. The objective of this study was to develop, analyze and validate new priors for Bayesian inference of such biomarkers. Theoretical analysis revealed a relationship between the estimates inferred from the model and the true values of measured quantities, and the impact of the priors. This led to a new prior encoding scheme that incorporates objectively measurable domain knowledge, i.e. by performing two measurements with a reference method, which imply scale of the prior distribution. Second, priors on parameters of systematic error are non-informative, which enables biomarker estimation from a set of different quantities. Analysis showed that the volume of nucleus basalis of Meynert, which is reduced in early stages of Alzheimer's dementia and Parkinson's disease, is inter-related and could be inferred from compartmental brain volume measurements performed on routine clinical MR scans. Another experiment showed that total lesion load, associated to future disability progression in multiple sclerosis patients, could be inferred from lesion volume measurements based on multiple automated MR scan segmentations. Besides, figures of merit derived from the estimates could, without comparing against reference gold standard segmentations, identify the best performing lesion segmentation method. The proposed new priors substantially simplify the application of Bayesian inference for latent biomarkers and thus open an avenue for clinical implementation of new biomarkers, which may ultimately advance the evidence-based medicine.

Reference-free error estimation for multiple measurement methods

We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works, the random errors are modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on six synthetic and one clinical dataset containing measurements of total lesion load, a biomarker of neurodegenerative diseases, which was obtained with four automatic methods by analyzing brain magnetic resonance images. The estimates of bias and random error were in a good agreement with the corresponding least squares regression estimates against a reference.

A variational approach to liver segmentation using statistics from multiple sources

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of [Formula: see text], the best root mean square symmetric surface distance (RMSD) of [Formula: see text] mm, the best maximum symmetric surface distance (MSD) of [Formula: see text] mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of [Formula: see text] mm, the best RMSD of [Formula: see text] mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

PURPOSE

Accurate segmentation of left ventricle (LV) is essential for the cardiac function analysis. However, it is labor intensive and time consuming for radiologists to delineate LV boundary manually. In this paper, we present a novel self-correcting framework for the fully automatic LV segmentation.

METHODS

Firstly, a time-domain method is designed to extract a rectangular region of interest around the heart. Then, the simplified pulse-coupled neural network (SPCNN) is employed to locate the LV cavity. Different from the existing approaches, SPCNN can realize the self-correcting segmentation due to its parameter controllability. Subsequently, the post-processing based on the maximum gradient searching is proposed to obtain the accurate endocardium. Finally, a new external force based on the shape similarity is defined and integrated into the gradient vector flow (GVF) snake with the balloon force to segment the epicardium.

RESULTS

We obtain encouraging segmentation results tested on the database provided by MICCAI 2009. The average percentage of good contours is 92.26 %, the average perpendicular distance is 2.38 mm, and the overlapping dice metric is 0.89. Besides, the experiment results show good correlations between the automatic segmentation and the manual delineation (for the LV ejection fraction and the LV myocardial mass, the correlation coefficients R are 0.9683 and 0.9278, respectively).

CONCLUSION

We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.