This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem

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Ultrasound image segmentation: a survey

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1053/jhep.2002.32527

Apolipoprotein synthesis in nonalcoholic steatohepatitis

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

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Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Due to parallel advances in signal processing and computer hardware in the last 15 years, quantitative ultrasound techniques have reached maturity, allowing for the construction of quantitative maps or images of soft tissues. This book will focus on 5 modern research topics related to quantitative ultrasound of soft tissues: - Spectral-based methods for tissue characterization, tissue typing, cancer detection, etc.; - Envelope statistics analysis as a means of quantifying and imaging tissue properties; - Ultrasound elastography for quantifying elastic properties of tissues (several clinical ultrasound scanners now display elastography images); - Scanning acoustic microscopy for forming images of mechanical properties of soft tissues with micron resolution (desktop size scanners are now available); and - Ultrasound computer tomography for breast cancer imaging (new ultrasound tomography systems have been developed and are currently under evaluation clinically).

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Quantitative Ultrasound in Soft Tissues

Ultrasound (US) spectral analysis methods are used to analyze the radiofrequency (RF) data collected from cell pellets exposed to chemotherapeutics that induce apoptosis and other chemicals that induce nuclear transformations. Calibrated backscatter spectra from regions-of-interest (ROI) were analyzed using linear regression techniques to calculate the spectral slope and midband fit. Two f/2 transducers, with operating frequencies of 30 and 34 MHz (relative bandwidths of 93% and 78%, respectively) were used with a custom-made imaging system that enabled the collection of the raw RF data. For apoptotic cells, the spectral slope increased from 0.37 dB/MHz before drug exposure to 0.57 dB/MHz 24 h after, corresponding to a change in effective scatterer radius from 8.7 to 3.2 m. The midband fit increased in a time-dependent fashion, peaking at 13dB 24 h after exposure. The statistical deviation of the spectral parameters was in close agreement with theoretical predictions. The results provide a framework for using spectral parameter methods to monitor apoptosis in in vitro and in in vivo systems and are being used to guide the design of system and signal analysis parameters. (E-mail: mkolios@ryerson.ca) © 2002 World Federation for Ultrasound in Medicine & Biology.

Ultrasonic spectral parameter characterization of apoptosis.

A study was performed to find and test quantitative methods of analysing echographic signals for the differentiation of diffuse liver diseases. An on-line data acquisition system was used to acquire radiofrequency (RF) echo signals from volunteers and patients. Several methods to estimate the frequency-dependent attenuation coefficient were evaluated, in which a correction for the frequency and depth-dependent diffraction and focusing effects caused by the sound beam was applied. Using the estimated value of the attenuation coefficient the RF signals themselves were corrected to remove the depth dependencies caused by the sound beam and by the frequency-dependent attenuation. After this preprocessing the envelope of the corrected RF signals was calculated and B-mode images were reconstructed. The texture was analysed in the axial direction by first- and second-order statistical methods. The accuracy and precision of the attenuation methods were assessed by using computer simulated RF signals and RF data obtained from a tissue-mimicking phantom. The phantom measurements were also used to test the performance of the methods to correct for the depth dependencies.

http://iopscience.iop.org/article/10.1088/0031-9155/36/8/002/pdf

Ultrasound attenuation and texture analysis of diffuse liver disease methods and preliminary results

Radio frequency (RF) echograms were acquired from human subjects without liver pathology (n = 126), who were in the range of 20 to 84 years of age. After appropriate correction for the equipment settings and performance characteristics, acoustospectrographic parameters were estimated. The data were corrected for the frequency-dependent attenuation and then software demodulated. Image texture parameters were calculated based on the assumption of a particular tissue model, and purely statistical parameters were also estimated. The data thus obtained from each subject were corrected for the age trend that was assessed in an earlier publication by the authors. A total of 18 parameters was considered and the correlations were analyzed. It is concluded that nine parameters could be identified which did not strongly correlate with each other. This conclusion is supported by analysis of correlation data from patients. In a companion paper, a discriminant analysis is reported, based on the selected parameters (nine) and applied to the differentiation between normals and various classes of diffuse liver disease.

Correlations between acoustic and texture parameters from RF and B-mode liver echograms

Quantitative acoustic parameters and image texture parameters were used in a linear discriminant analysis. This analysis was applied to detect retrospectively the classes of diffuse liver disease against a population of normal livers. Three different sets of parameters were employed. The first set was selected by the authors, and the other two were taken from the literature. The area under the Receiver Operating Characteristic (ROC) (or percentage correct classification) obtained with the first set ranged from 88% to 97%, depending on the disease class. It is concluded that the first-order statistical parameters of the image texture (diffuse scattering model) together with the slope of the attenuation coefficient are the most important parameters. As an alternative to the texture parameters, the backscattering parameters (second set of parameters) also yielded a comparably high score. The texture analysis involving structural scattering (third set of parameters) produced a lower percentage of correct classification. The overall conclusion is that the methods devised might be used for prospective diagnosis.

Detection of diffuse liver disease by quantitative echography: dependence on a Priori choice of parameters.

RATIONALE AND OBJECTIVES The detectability of diffuse liver diseases by quantitative echography was retrospectively investigated using scans of patients with known pathologic findings (n = 103) and of normal subjects (n = 129). The authors determined the best set of quantitative parameters for this task. METHODS Quantitative echography was comprised of acoustospectrographic parameters (frequency dependence of attenuation and backscattering) and image texture parameters. The disease processes studied included: acute hepatitis, hepatitis/cirrhosis, alcoholic hepatitis/cirrhosis, primary biliary cirrhosis, and steatosis. RESULTS Correct differentiation of these diseases ranged from 88% to 97%. Correlations between histologic grading and echographic parameters were poor. With only one exception, the differentiation between any two of the diseases could be made in 60% to 99% of cases. Different parameters better differentiated abnormal from normal scans than among diseases. CONCLUSIONS The detection of diffuse liver diseases can be based on echographic parameters, related to a diffuse scattering model, whereas the differentiation among diseases needs additional parameters derived from a structural scattering model. Further studies are indicated to assess the prospective potential of the devised methods.

Detection and differentiation of diffuse liver disease by quantitative echography. A retrospective assessment.

The values of acoustic and image texture parameters were estimated from liver scans of healthy subjects. The values were obtained after appropriate preprocessing of the radio frequency echograms by an on-line computerized system. The preprocessing comprised a correction for the Time Gain Compensation (TGC), the beam diffraction and the frequency dependent attenuation in the Region of Interest (ROI). The intra- and interindividual variability of the parameter values appeared to be of the same order of magnitude, but significantly larger than the variability assessed by measurements of a homogeneously scattering tissue mimicking phantom. Significant temporal effects were found for all the parameters, which consistently occurred during the morning. These results are discussed in relation to the circadian rhythm of the glycogen content and of the hepatic circulation. All the parameters appeared to be significantly correlated to age. The slope of the regression ranged from 3.6% per decade (attenuation coefficient) to 7.6% per decade (mean echolevel). A tentative explanation to these results is presented: the increased stiffness of hepatic vasculature with age.

B.J. Oosterveld

Papers

Ultrasound attenuation and texture analysis of diffuse liver disease methods and preliminary results

Correlations between acoustic and texture parameters from RF and B-mode liver echograms

Detection of diffuse liver disease by quantitative echography: dependence on a Priori choice of parameters.

Detection and differentiation of diffuse liver disease by quantitative echography. A retrospective assessment.

Variability of quantitative echographic parameters of the liver: intra- and interindividual spread, temporal- and age-related effects.