Intra-abdominal fat: Comparison of computed tomography fat segmentation and bioimpedance spectroscopy

Background: Intra-abdominal fat is an important factor in determining the metabolic syndrome/insulin resistance, and thus the risk of diabetes and ischaemic heart disease. Computed Tomography (CT) fat segmentation represents a defined method of quantifying intra-abdominal fat, with attendant radiation risks. Bioimpedance spectroscopy may offer a method of assessment without any risks to the patients. A comparison is made of these two methods.Methods: This was a preliminary study of the utility of multifrequency bioimpedance spectroscopy of the mid abdomen as a measure of intraabdominal fat, by comparison with fat segmentation of an abdominal CT scan in the -30 to -190 HU range.Results: There was a significant (P < 0.01) correlation between intra-abdominal fat and mid-upper arm circumference, as well as the bioimpedance parameter, the R/S ratio. Multivariate analysis showed that these were the only independant variables and allowed the derivation of a formula to estimate intra-abdominal fat: IAF = 0.02 × MAC − 0.757 × R/S + 0.036.Conclusions: Circumabdominal bioimpedance spectroscopy may prove a useful method of assessing intra-abdominal fat, and may be suitable for use in studies to enhance other measures of body composition, such as mid-upper arm circumference

Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The S{\o}rensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine

Which anthropometric measurements including visceral fat, subcutaneous fat, body mass index, and waist circumference could predict the urinary stone composition most?

BACKGROUND: Although there is growing evidence of relationship between obesity and some specific stone compositions, results were inconsistent. Due to a greater relationship between metabolic syndrome and some specific stone type, obesity measured by body mass index (BMI) has limitation in determining relationship between obesity and stone compositions. The aim of this study was to determine the relationship among BMI, visceral fat, and stone compositions. METHODS: We retrospectively reviewed data of patients with urinary stone removed over a 5 year period (2011-2014). Data on patient age, gender, BMI, urinary pH, stone composition, fat volumes (including visceral fat, subcutaneous fat, total fat, waist circumference), and ratio for visceral to total fat using computed tomography based delineation were collected. To figure out the predicting factor while adjusting other confounding factors, discriminant analysis was used. RESULTS: Among 262 cases, average age was 52.21 years. Average BMI and visceral fat were 25.03 cm(2) and 124.75 cm(2), respectively. By chi square test, there was significant (p < 0.001) difference in stone types according to sex. By ANOVA test, BMI, visceral fat, visceral to subcutaneous fat ratio, the percentage of visceral fat and total fat showed significant association with stone types. By discriminant analysis, visceral fat was proved to be a powerful factor to predict stone composition (structure matrix of visceral fat = -0.735) with 42.0% of predictive value. CONCLUSION: Visceral fat adiposity strongly related with uric acid stone and has better predictive value than BMI or urinary pH to classify the types of stone.ope

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

DEVELOPING MEDICAL IMAGE SEGMENTATION AND COMPUTER-AIDED DIAGNOSIS SYSTEMS USING DEEP NEURAL NETWORKS

Diagnostic medical imaging is an important non-invasive tool in medicine. It provides doctors (i.e., radiologists) with rich diagnostic information in clinical practice. Computer-aided diagnosis (CAD) schemes aim to provide a tool to assist the doctors for reading and interpreting medical images. Traditional CAD schemes are based on hand-crafted features and shallow supervised learning algorithms. They are greatly limited by the difficulties of accurate region segmentation and effective feature extraction. In this dissertation, our motivation is to apply deep learning techniques to address these challenges. We comprehensively investigated the feasibilities of applying deep learning technique to develop medical image segmentation and computer-aided diagnosis schemes for different imaging modalities and different tasks. First, we applied a two-step convolutional neural network architecture for selection of abdomen part and segmentation of subtypes of adipose tissue from abdominal CT images. We demonstrated high agreement between the segmentation generated by human and by our proposed deep learning models. Second, we explored to combine transfer learning technique with traditional hand-crafted features to improve the accuracy of breast mass classification from digital mammograms. Our results show that the ensemble of hand-crafted features and transferred features yields improvement of prediction performances. Third, we proposed a 3D fully convolutional network architecture with a novel coarse-to-fine residual module for prostate segmentation from MRI. State-of-art segmentation accuracy was obtained by using this model. We also investigated the feasibilities of applying fully convolutional network for prostate cancer detection based on multi-parametric MRI and obtained promising detection accuracy. Last, we proposed a novel cascaded neural network architecture with post-processing steps for nuclear segmentation from histology images. Superiority of the model was demonstrated by experiments. In summary, these study results demonstrated that deep learning is a very promising technology to help significantly improve efficacy of developing computer-aided diagnosis schemes of medical images and achieve higher performance