Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography

Zhao, Binsheng; Colville, Jane; Kalaigian, J.; Curran, Sean; Jiang, Li; Kijewski, Peter K.; Schwartz, Lawrence H.

doi:10.1097/01.rct.0000228164.08968.e8

Cited by 70 publications

(69 citation statements)

References 15 publications

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“…Recent research on influence of different tissues [34] on ECG signals suggests that muscle, lungs and fat tissues are more important than the others. Most of these tissues can be segmented automatically [35][36][37][38]. In this paper, we will focus on segmentation of abdominal parenchymal organs.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

2016

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Abstract:In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.

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Section: Segmentation Techniquesmentioning

confidence: 99%

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

2016

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“…Recently imaging technologies have emerged as powerful tools for refined adipose assessment (Zhao et al, 2006;Luu et al, 2009). Imaging can provide not only the size of an adipose depot, but also its location.…”

Section: Adipose Imaging Techniquementioning

confidence: 99%

Adipose Measurement Using Micro MRI

Tang¹,

Moats²

2012

Advanced Image Acquisition, Processing Techniques and Applications I

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“…However, the manual segmentation of large databases of CT images used in these studies is not practical and hence automatic segmentation methods are needed. Although, the segmentation of the fat region using automatic methods [3], [4] is relatively straightforward due to the unique HU range of the fat tissue [−190, −30], the automatic segmentation of the muscle region is quite challenging as there exists significant overlap between the HU ranges of the muscle tissue [−29, 150] and surrounding organs (see Figure 1c).…”

Section: Introductionmentioning

confidence: 99%

FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images

Popuri

Cobzaş

Jägersand

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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The estimation of body composition (i.e., proportions of muscle and fat tissues) in cancer patients has important clinical and research applications. In particular, chemotherapy drug dosage is determined after taking into account the muscle and fat proportions in the patient's body. Recently, there has been considerable interest in studying the correlation between survival and body composition in cancer patients. We propose a fully automated framework for segmentation and quantification of muscle and fat tissues in thoracic CT images. A novel approach based on statistical deformation model (SDM) constrained deformable registration using the finite element method (FEM) is proposed. We obtained very good segmentation results with Jaccard scores of 94.95% for muscle and 94.82% for fat tissues respectively on a large data set of 116 thoracic CT images.

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Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography

Abstract: Preliminary results have shown that total compartmental fat, including visceral and subcutaneous fat, can be automatically and accurately segmented on volumetric CT.

Cited by 70 publications

References 15 publications

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Adipose Measurement Using Micro MRI

FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images

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