Ultrasound image segmentation using Gamma combined with Bayesian model for focused-ultrasound-surgery lesion recognition

This study aims to investigate the feasibility of combined segmentation for the separation of lesions from non-ablated regions, which allows surgeons to easily distinguish, measure, and evaluate the lesion area, thereby improving the quality of high-intensity focused-ultrasound (HIFU) surgery used for the non-invasive tumor treatment. Given that the flexible shape of the Gamma mixture model (GΓMM) fits the complex statistical distribution of samples, a method combining the GΓMM and Bayes framework is constructed for the classification of samples to obtain the segmentation result. An appropriate normalization range and parameters can be used to rapidly obtain a good performance of GΓMM segmentation. The performance values of the proposed method under four metrics (Dice score: 85%, Jaccard coefficient: 75%, recall: 86%, and accuracy: 96%) are better than those of conventional approaches including Otsu and Region growing. Furthermore, the statistical result of sample intensity indicates that the finding of the GΓMM is similar to that obtained by the manual method. These results indicate the stability and reliability of the GΓMM combined with the Bayes framework for the segmentation of HIFU lesions in ultrasound images. The experimental results show the possibility of combining the GΓMM with the Bayes framework to segment lesion areas and evaluate the effect of therapeutic ultrasound.

Verifying Clinically Derived Reference Intervals for Daily Excretion Rates of Fractionated Metanephrines Using Modern Indirect Reference Interval Models

OBJECTIVES

Biochemical testing of urinary metanephrines is useful in the diagnosis and monitoring of pheochromocytoma and paragangliomas. We investigated the feasibility of mixture decomposition (ie, indirect) methods in verifying clinically derived reference intervals for urinary deconjugated metanephrine metabolites.

METHODS

Urinary 24-hour metanephrine and normetanephrine excretion results were extracted from our data warehouse and intervals were estimated by the modern variant of the Hoffmann method, maximum likelihood estimation (MLE), and gamma mixture model using R software.

RESULTS

Hoffmann, MLE, and gamma mixture models provided metanephrine and normetanephrine intervals that closely matched those derived from clinical studies. However, three-component MLE and gamma models were required for normetanephrine in adult women because the Hoffmann method was not suitable. Some data transformations caused blending of the mixed distributions and subsequent widening of the reference interval estimation, emphasizing the importance of careful data transformation for Hoffmann and MLE analyses. Gamma mixture models gave overall good agreement without the need for data transformation.

CONCLUSIONS

Indirect methods have utility in verifying reference intervals in 24-hour urine specimens collected by patients. We emphasize the benefits of applying multiple decomposition methods to corroborate findings and careful application of data transformation when using Gaussian-based models.

A second look at the final number of cells in a fluctuation experiment

In a fluctuation experiment, the number of cells existing in a culture immediately before plating (commonly known as Nt) varies across the parallel cultures. However, most existing mathematical models for fluctuation assay data do not recognize the variation in Nt. Despite repeated attempts in the past to integrate this source of variability in the estimation of microbial mutation rates, several questions of practical importance remain unanswered. The present investigation finds that the variation needs accounting for only when the coefficient of variation for Nt is large, and experimental data suggest that the coefficient of variation is often moderate or small. Moreover, an increase in the inoculum size can reduce the coefficient of variation. Through extensive simulation, several existing methods that accommodate the variation in Nt are compared. It was found that a newly devised likelihood method based on the existing gamma mixture model outperforms other existing methods. The investigation focuses on the estimation of mutation rates using the Lea-Coulson model, under which mutation is selectively neutral; however, the paper also explores the major findings' implications for the comparison of mutation rates using the likelihood ratio test, and for the estimation of mutation rates using the Mandelbrot-Koch model that allows for non-neutral mutations.

Improved Segmentation of Multiple Cavities of the Heart in Wide-View 3-D Transesophageal Echocardiograms

Minimally invasive interventions in the heart such as in electrophysiology are becoming more and more important in clinical practice. Currently, preoperative computed tomography angiography (CTA) is used to provide anatomic information during electrophysiology interventions, but this does not provide real-time feedback and burdens the patient with additional radiation and side effects of the contrast agent. Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for visualization of anatomic structures and instruments in real time, but some cavities, especially the left atrium, suffer from the limited coverage of the 3-D TEE volumes. This leads to difficulty in segmenting the left atrium. We propose replacing or complementing pre-operative CTA imaging with wide-view TEE. We tested this proposal on 20 patients for which TEE image volumes covering the left atrium and CTA images were acquired. The TEE images were manually registered, and wide-view volumes were generated. Five heart cavities in single-view and wide-view TEE were segmented and compared with atlas based-segmentations derived from the CTA images. We found that the segmentation accuracy (Dice coefficients) improved relative to segmentation of single-view images by 5, 15 and 9 percentage points for the left atrium, right atrium and aorta, respectively. Average anatomic coverage was improved by 2, 29, 62 and 49 percentage points for the right ventricle, left atrium, right atrium and aorta, respectively. This finding confirms that wide-view 3-D TEE can be useful in supporting electrophysiology interventions.

Fully automatic detection of salient features in 3-d transesophageal images

Most automated segmentation approaches to the mitral valve and left ventricle in 3-D echocardiography require a manual initialization. In this article, we propose a fully automatic scheme to initialize a multicavity segmentation approach in 3-D transesophageal echocardiography by detecting the left ventricle long axis, the mitral valve and the aortic valve location. Our approach uses a probabilistic and structural tissue classification to find structures such as the mitral and aortic valves; the Hough transform for circles to find the center of the left ventricle; and multidimensional dynamic programming to find the best position for the left ventricle long axis. For accuracy and agreement assessment, the proposed method was evaluated in 19 patients with respect to manual landmarks and as initialization of a multicavity segmentation approach for the left ventricle, the right ventricle, the left atrium, the right atrium and the aorta. The segmentation results revealed no statistically significant differences between manual and automated initialization in a paired t-test (p > 0.05). Additionally, small biases between manual and automated initialization were detected in the Bland-Altman analysis (bias, variance) for the left ventricle (-0.04, 0.10); right ventricle (-0.07, 0.18); left atrium (-0.01, 0.03); right atrium (-0.04, 0.13); and aorta (-0.05, 0.14). These results indicate that the proposed approach provides robust and accurate detection to initialize a multicavity segmentation approach without any user interaction.