An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

A reliable M-mode ultrasound protocol for the assessment of diaphragm motion

Diaphragm is the principal inspiratory muscle. Different techniques have been used to assess diaphragm motion. Among them, M-mode ultrasound has gain particular interest since it is non-invasive and accessible. However it is operator-dependent and no objective acquisition protocol has been established. Purpose: to establish a reliable method for the assessment of the diaphragmatic motion via the M-mode ultrasound.

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.

Serum trace elements in dysphagic gastrostomy candidates before endoscopic gastrostomy for long term enteral feeding

Background & aims - Patients who underwent endoscopic gastrostomy (PEG) present protein-energy malnutrition, but little is known about Trace Elements (TE), Zinc (Zn), Copper (Cu), Selenium (Se), Iron (Fe), Chromium (Cr). Our aim was the evaluation of serum TE in patients who underwent PEG and its relationship with serum proteins, BMI and nature of underlying disorder. Methods - A prospective observational study was performed collecting: patient's age, gender, underlying disorder, NRS-2002, BMI, serum albumin, transferrin and TE concentration. We used ferrozine colorimetric method for Fe; Inductively Coupled Plasma-Atomic Emission Spectroscopy for Zn/Cu; Furnace Atomic Absorption Spectroscopy for Se/Cr. The patients were divided into head and neck cancer (HNC) and neurological dysphagia (ND). Results - 146 patients (89 males), 21–95 years: HNC-56; ND-90. Low BMI in 78. Low values mostly for Zn (n = 122) and Fe (n = 69), but less for Se (n = 31), Cu (n = 16), Cr (n = 7); low albumin in 77, low transferrin in 94 and 66 with both proteins low. Significant differences between the groups of underlying disease only for Zn (t140.326 = −2,642, p < 0.01) and a correlation between proteins and TE respectively albumin and Zn (r = 0.197, p = 0.025), and albumin and Fe (r = 0.415, p = 0.000). Conclusions - When gastrostomy was performed, patients display low serum TE namely Zn, but also Fe, less striking regarding others TE. It was related with prolonged fasting, whatever the underlying disease. Low proteins were associated with low TE. Teams taking care of PEG-patients should use Zn supplementation and include other TE evaluation as part of the nutritional assessment of PEG candidates.

Transcranial magnetic resonance guided focused ultrasound: neurosurgery applications

Mestrado em Radiações Aplicadas às Tecnologias da Saúde

Serum copper evolution in patients that underwent endoscopic gastrostomy for long term enteral feeding

Background and aims: Copper (Cu) is a well studied trace element but little is known about Cu evolution in long term endoscopic gastrostomy (PEG) feeding. We aimed to evaluate the evolution serum Cu since the gastrostomy until 12 weeks after the procedure in PEG patients fed with homemade meals. Methods: A prospective observational study was performed evaluating serum copper, albumin, transferrin and body mass index (BMI) at the time of the gastrostomy, 4 weeks and 12 weeks after. Data also included age, gender, NRS 2002 and nature of the underlying disease causing dysphagia: head and neck cancer (HNC) or neurological dysphagia (ND). After gastrostomy, patients were fed with homemade PEG meals. Results: One hundred and forty-six patients enrolled, 89 men, aged 21-95 years, 90 with neurologic dysphagia (ND), and 56 with head and neck cancer (HNC). 78 (53%) showed low BMI. Initially, Cu ranged 42-160 μg/dl (normal: 70-140 μg/dl); 130 patients (89%) presented normal Cu, 16 (11%) presented hypocupremia, 53% low albumin (n = 77), and 94 (65%) low transferrin. After 4 weeks, 93% presented normal Cu, 7% presented hypocupremia, low albumin was present in 34%, and low transferrin in 52%. After 12 weeks, 95% presented normal Cu, 5% presented hypocupremia, low albumin was present in 25%, and low transferrin in 32%. Comparing age, gender, underlying disease, BMI, albumin and transferrin, there were no significant differences on serum Cu. Conclusions: Most patients present normal serum Cu when gastrostomy is performed. For patients presenting hypocupremia before gastrostomy, homemade meals are effective for normalizing serum Cu.

Selenium in dysphagic patients who underwent endoscopic gastrostomy for long term enteral feeding

Background and aims - Endoscopic gastrostomy (PEG) patients usually present protein-energy malnutrition, but little is known about selenium deficiency. We aimed to assess serum selenium evolution when patients underwent PEG, after 4 and 12 weeks. We also evaluated the evolution of albumin, transferrin and Body Mass Index and the influence of the nature of the underlying disease. Methods - A blood sample was obtained before PEG (T0), after 4 (T1) and 12 (T3) weeks. Selenium was assayed using GFAAS (Furnace Atomic Absorption Spectroscopy). The PEG patients were fed through homemade meals. Patients were studied as a whole and divided into two groups: head and neck cancer (HNC) and neurological dysphagia (ND). Results - We assessed 146 patients (89 males), between 21-95 years old: HNC-56; ND-90. Normal values of selenium in 79% (n=115); low albumin in 77, low transferrin in 94, low values for both serum proteins in 66. Low BMI in 78. Selenium has slow evolution, with most patients still displaying normal Selenium at T3 (82%). Serum protein levels increase from T0 to T3, most patients reaching normal values. The nature of the underlying disease is associated with serum proteins but not with selenium. Conclusions - Low serum selenium is uncommon when PEG is performed, after 4 and 12 weeks of enteral feeding and cannot be related with serum proteins levels or dysphagia cause. Enteral nutrition using customized homemade kitchen meals is satisfactory to prevent or correct Selenium deficiency in the majority of PEG patients.

Serum trace elements in dysphagic patients that underwent endoscopic gastrostomy for long term enteral feeding

Patients who underwent endoscopic gastrostomy (PEG) present protein-energy malnutrition, but little is known about Trace Elements (TE), Zinc (Zn), Copper (Cu), Selenium (Se), Iron (Fe), Chromium (Cr). Our aim was the evaluation of serum TE in patients who underwent PEG and its relationship with serum proteins, BMI and nature of underlying disorder.