Dataset Information

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans.

ABSTRACT: BACKGROUND:Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices. METHODS:We present a new semi-automatic approach to defining the density cutoff between adipose tissue (AT) and lean tissue (LT) in such material. An intra-class correlation coefficient (ICC) was used to validate the method. The equations for estimating the whole-body composition volume and mass from areas measured in selected slices were modeled with ordinary least squares (OLS) linear regressions and support vector machine regression (SVMR). RESULTS AND DISCUSSION:The best predictive equation for total body AT volume was based on the AT area of a single slice located between the 4th and 5th lumbar vertebrae (L4-L5) and produced lower prediction errors (|PE| = 1.86 liters, %PE = 8.77) than previous equations also based on CT scans. The LT area of the mid-thigh provided the lowest prediction errors (|PE| = 2.52 liters, %PE = 7.08) for estimating whole-body LT volume. We also present equations to predict total body AT and LT masses from a slice located at L4-L5 that resulted in reduced error compared with the previously published equations based on CT scans. The multislice SVMR predictor gave the theoretical upper limit for prediction precision of volumes and cross-validated the results.

SUBMITTER: Lacoste Jeanson A

PROVIDER: S-EPMC5438582 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans.

Lacoste Jeanson Alizé A Dupej Ján J Villa Chiara C Brůžek Jaroslav J

PeerJ 20170518

<h4>Background</h4>Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these ...[more]

PMID: 28533960

Similar Datasets

Project description:BackgroundPulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.MethodsThe proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation.ResultsFor the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm.ConclusionsThis study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Project description:The current standard for measuring coronary artery calcification to determine the extent of atherosclerosis is by calculating the Agatston score from computed tomography (CT). However, the Agatston score disregards pixel values less than 130 Hounsfield Units (HU) and calcium regions less than 1 mm2. Due to this thresholding, the score is not sensitive to small, weakly attenuating regions of calcium deposition and may not detect nascent micro-calcification. A recently proposed metric called the spatially weighted calcium score (SWCS) also utilizes CT but does not include a threshold for HU and does not require elevated signals in contiguous pixels. Thus, the SWCS is sensitive to weakly attenuating, smaller calcium deposits and may improve the measurement of coronary heart disease risk. Currently, the SWCS is underutilized owing to the added computational complexity. To promote translation of the SWCS into clinical research and reliable, repeatable computation of the score, the aim of this study was to develop a semi-automatic graphical tool that calculates both the SWCS and the Agatston score. The program requires gated cardiac CT scans with a calcium hydroxyapatite phantom in the field of view. The phantom allows for deriving a weighting function, from which each pixel's weight is adjusted, allowing for the mitigation of signal variations and variability between scans. With all three anatomical views visible simultaneously, the user traces the course of the four main coronary arteries by placing points or regions of interest. Features such as scroll-to-zoom, double-click to delete, and brightness/contrast adjustment, along with written guidance at every step, make the program user-friendly and easy to use. Once tracing the arteries is complete, the program generates reports, which include the scores and snapshots of any visible calcium. The SWCS may reveal the presence of subclinical disease, which may be used for early intervention and lifestyle changes.

Project description:Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

Dataset Information

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans.

Publications

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets