Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans

Mensink, Sanne D.; Spliethoff, Jarich W.; Belder, Ruben; Klaase, Joost M.; Bezooijen, Roland; Slump, Cornelis H.

doi:10.1117/12.878017

Cited by 8 publications

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“…Fully automated analysis of body composition has been attempted many times in the past. Older methods utilize classical image processing and binary morphological operations [23][24][25] in order to isolate the SAT and VAT from total adipose tissue (TAT). Other studies use prior knowledge about contours and shapes and actively fit a contour or template to a given CT image [26][27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

et al. 2020

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Objectives Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic 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. Methods 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. Results The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Conclusions 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 analyzing body composition in the clinical routine. Key Points • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

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Section: Discussionmentioning

confidence: 99%

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

et al. 2020

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“…Several fully automated segmentation methods to assess body composition using CT examinations have been proposed; however, limitations of traditional image-processing techniques and the complexity of abdominal imaging have prevented widespread use. Adipose tissue is primarily identified with threshold-based techniques (10)(11)(12)(13)(14)(15), and atlasbased techniques are used to isolate the abdominal muscles (16)(17)(18)(19). However, anatomic variability in the abdomen poses a substantial challenge to automated segmentation, and manual correction is almost always required.…”

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confidence: 99%

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

et al. 2019

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To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods: For this retrospective study, a convolutional neural network based on the U-Net architecture was trained to perform abdominal segmentation on a data set of 2430 two-dimensional CT examinations and was tested on 270 CT examinations. It was further tested on a separate data set of 2369 patients with hepatocellular carcinoma (HCC). CT examinations were performed between 1997 and 2015. The mean age of patients was 67 years; for male patients, it was 67 years (range, 29-94 years), and for female patients, it was 66 years (range, 31-97 years). Differences in segmentation performance were assessed by using twoway analysis of variance with Bonferroni correction. Results: Compared with reference segmentation, the model for this study achieved Dice scores (mean 6 standard deviation) of 0.98 6 0.03, 0.96 6 0.02, and 0.97 6 0.01 in the test set, and 0.94 6 0.05, 0.92 6 0.04, and 0.98 6 0.02 in the HCC data set, for the subcutaneous, muscle, and visceral adipose tissue compartments, respectively. Performance met or exceeded that of expert manual segmentation. Conclusion: Model performance met or exceeded the accuracy of expert manual segmentation of CT examinations for both the test data set and the hepatocellular carcinoma data set. The model generalized well to multiple levels of the abdomen and may be capable of fully automated quantification of body composition metrics in three-dimensional CT examinations.

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“…[32][33][34][35][36] The central idea of those methods is to seek the abdominal wall which separates SAT and VAT in the abdominal region. Many strategies, including curve smoothing, 32 morphological operations, 33 and mask matching, [34][35][36] have been proposed to automatically achieve this objective. However, these methods cannot be applied to muscle and bone segmentation simultaneously with SAT/VAT segmentation/separation, and the performance is highly dependent on the accuracy of the location of the abdominal wall.…”

Section: B Related Workmentioning

confidence: 99%

“…However, the accuracy of thresholding is poor, and the manual operation method is labor‐intensive, error‐prone, and impractical when applied to the whole body or to a large number of imaging studies. Some methods aim at automatically segmenting adipose tissues, more specifically with a focus on separating SAT and VAT, from CT images . The central idea of those methods is to seek the abdominal wall which separates SAT and VAT in the abdominal region.…”

Section: Introductionmentioning

confidence: 99%

Quantification of body‐torso‐wide tissue composition on low‐dose CT images via automatic anatomy recognition

et al. 2019

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Purpose Quantification of body composition plays an important role in many clinical and research applications. Radiologic imaging techniques such as Dual‐energy X‐ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computed tomography (CT) imaging make accurate quantification of the body composition possible. However, most current imaging‐based methods need human interaction to quantify multiple tissues. When dealing with whole‐body images of many subjects, interactive methods become impractical. This paper presents an automated, efficient, accurate, and practical body composition quantification method for low‐dose CT images. Method Our method, named automatic anatomy recognition body composition analysis (AAR‐BCA), aims to quantify four tissue components in body torso (BT) — subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), bone tissue, and muscle tissue — from CT images of given whole‐body positron emission tomography/computed tomography (PET/CT) acquisitions. AAR‐BCA consists of three key steps — modeling BT with its ensemble of key objects from a population of patient images, recognition or localization of these objects in a given patient image I, and delineation and quantification of the four tissue components in I guided by the recognized objects. In the first step, from a given set of patient images and the associated delineated objects, a fuzzy anatomy model of the key object ensemble, including anatomic organs, tissue regions, and tissue interfaces, is built where the objects are organized in a hierarchical order. The second step involves recognizing, or finding roughly the location of, each object in any given whole‐body image I of a patient following the object hierarchy and guided by the built model. The third step makes use of this fuzzy localization information of the objects and the intensity distributions of the four tissue components, already learned and encoded in the model, to optimally delineate in a fuzzy manner and quantify these components. All parameters in our method are determined from training datasets. Results Thirty‐eight low‐dose CT images from different subjects are tested in a fivefold cross‐validation strategy for evaluating AAR‐BCA with a 23–15 train‐test dataset division. For BT, over all objects, AAR‐BCA achieves a false‐positive volume fraction (FPVF) of 3.7% and false‐negative volume fraction (FNVF) of 3.8%. Notably, SAT achieves both a FPVF and FNVF under 3%. For bone tissue, it achieves a FPVF and a FNVF both under 3.5%. For VAT tissue, the FNVF of 4.8% is higher than for other objects and so also for muscle (4.7%). The level of accuracy for the four tissue components in individual body subregions mostly remains at the same level as for BT. The processing time required per patient image is under a minute. Conclusions Motivated by applications in cancer and systemic diseases, our goal in this paper was to seek a practical method for body composition quantification which is automated, accurate, and efficient, and works on BT in low‐dose CT. The pro...

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Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans

Cited by 8 publications

References 0 publications

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

Quantification of body‐torso‐wide tissue composition on low‐dose CT images via automatic anatomy recognition

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