Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

CEUS LI-RADS: a pictorial review

Contrast-enhanced ultrasound (CEUS) greatly improved the diagnostic accuracy of US in the detection and characterization of focal liver lesions (FLLs), and it is suggested and often included in many international guidelines as an important diagnostic tool in the imaging work-up of cirrhotic patients at risk for developing hepatocellular carcinoma (HCC). In particular, CEUS Liver Imaging Reporting and Data System (LI-RADS) provides standardized terminology, interpretation, and reporting for the diagnosis of HCC. The aim of this pictorial essay is to illustrate CEUS features of nodules discovered at US in cirrhotic liver according to LI-RADS categorization

Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound (QUS) and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the nerves were extracted to prepare histology sections. 85 fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin, and ultrasound data to calculate backscatter coefficient (-24.89 dB $\pm$ 8.31), attenuation coefficient (0.92 db/cm-MHz $\pm$ 0.04), Nakagami parameter (1.01 $\pm$ 0.18) and entropy (6.92 $\pm$ 0.83), as well as B-mode texture features obtained via the gray level co-occurrence matrix algorithm. Significant Spearman's rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R=-0.68), entropy (R=-0.51), and for several texture features. Our study demonstrates that QUS may potentially provide information on structural components of nerve fascicles

Diagnostic Accuracy of CEUS LI-RADS for the Characterization of Liver Nodules 20 mm or Smaller in Patients at Risk for Hepatocellular Carcinoma.

Background: American College of Radiology contrast agent–enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) was developed to improve the accuracy of hepatocellular carcinoma (HCC) diagnosis at contrast agent2enhanced US. However, to the knowledge of the authors, the diagnostic accuracy of the system in characterization of liver nodules 20 mm or smaller has not been fully evaluated. Purpose: To evaluate the diagnostic accuracy of CEUS LI-RADS in diagnosing HCC in liver nodules 20 mm or smaller in patients at risk for HCC. Materials and Methods: Between January 2015 and February 2018, consecutive patients at risk for HCC presenting with untreated liver nodules 20 mm or less were enrolled in this retrospective double-reader study. Each nodule was categorized according to the CEUS LI-RADS and World Federation for Ultrasound in Medicine and Biology (WFUMB)–European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) criteria. Diagnostic performance of CEUS LI-RADS and WFUMB-EFSUMB characterization was evaluated by using tissue histologic analysis, multiphase contrast-enhanced CT and MRI, and imaging follow-up as reference standard and compared by using McNemar test. Results: The study included 175 nodules (mean diameter, 16.1 mm 6 3.4) in 172 patients (mean age, 51.8 years 6 10.6; 136 men). The sensitivity of CEUS LR-5 versus WFUMB-EFSUMB criteria in diagnosing HCC was 73.3% (95% confidence inter-val [CI]: 63.8%, 81.5%) versus 88.6% (95% CI: 80.9%, 94%), respectively (P, .001). The specificity of CEUS LR-5 versus WFUMB-EFSUMB criteria was 97.1% (95% CI: 90.1%, 99.7%) versus 87.1% (95% CI: 77%, 94%), respectively (P = .02). No malignant lesions were found in CEUS LR-1 and LR-2 categories. Only two nodules (of 41; 5%, both HCC) were malignant in CEUS LR-3 category. The incidences of HCC in CEUS LR-4, LR-5, and LR-M were 48% (11 of 23), 98% (77 of 79), and 75% (15 of 20), respectively. Two of 175 (1.1%) histologic analysis2confirmed intrahepatic cholangiocarcinomas were categorized as CEUS LR-M by CEUS LI-RADS and misdiagnosed as HCC by WFUMB-EFSUMB criteria. Conclusion: The contrast-enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) algorithm was an effective tool for characterization of small (≤20 mm) liver nodules in patients at risk for hepatocellular carcinoma (HCC). Compared with World Federation for Ultrasound in Medicine and Biology2European Federation of Societies for Ultrasound in Medicine and Biology criteria, CEUS LR-5 demonstrated higher specificity for diagnosing small HCCs with lower sensitivity

A Bibliometric Analysis of the Top 100 Cited Articles on Hepatic Magnetic Resonance Imaging.

The purpose of this study is to guide the readers to the impact of the articles published on hepatic magnetic resonance imaging (MRI). We searched Scopus using 10 different search terms for hepatic MRI. The selected studies were thoroughly reviewed by two independent authors and any disagreement was sorted out by mutual consensus. The list of articles and journals was downloaded into an excel spreadsheet. Only the top 100 cited articles were selected by mutual consensus among all the authors. These articles were further read in the full-text form and were further categorized into subgroups. Three authors independently reviewed the top 100 selected articles, and subsequently data was extracted from them and analyzed. Our study showed that the highest number of top 100 cited articles on hepatic MRI were from Radiology (30 articles) followed by European Radiology (14 articles). The American Journal of Roentgenology, Radiographics, and Journal of Magnetic Resonance had seven articles each. The United States had the highest number of articles by region. Nineteen other journals contributed only one article each to the list of top 100 cited articles. The contribution of authors to the top 100 cited articles was reviewed; all the authors contributing with more than two articles to the highly cited articles are given in Table 3 in the supplementary material. The maximum number of articles were published during 2009 (14 articles), and for a five-year period, the maximum contribution was made during 2008-2013 (44 articles). Our analysis gives an insight on the frequency of citations of top articles on hepatic MRI, categorizes the subtopics, the timeline of the publications, and contributions from different geographic distributions

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

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

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