Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

Hussein, Sarfaraz; Green, Aileen; Watane, Arjun; Reiter, David A.; Chen, Xinjian; Papadakis, Georgios Z.; Wood, Bradford J.; Cypess, Aaron M.; Osman, Medhat; Bağcı, Ulaş

doi:10.1109/tmi.2016.2636188

Cited by 24 publications

(29 citation statements)

References 49 publications

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“…A location error of less than 5 NoS seems acceptable. It is noted that the localization error for thorax presented in this paper are more attractive when comparing to the more recent report in reference [10] with body region localization error of 47.01mm.…”

Section: Experiments and Resultsmentioning

confidence: 62%

Automatic thoracic body region localization

et al. 2017

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Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head & neck, thorax, abdomen, pelvis, and extremities. For automating image analysis and consistency of results, standardizing definitions of body regions and the various anatomic objects, tissue regions, and zones in them becomes essential. Assuming that a standardized definition of body regions is available, a fundamental early step needed in automated image and object analytics is to automatically trim the given image stack into image volumes exactly satisfying the body region definition. This paper presents a solution to this problem based on the concept of virtual landmarks and evaluates it on whole-body positron emission tomography/computed tomography (PET/CT) scans. The method first selects a (set of) reference object(s), segments it (them) roughly, and identifies virtual landmarks for the object(s). The geometric relationship between these landmarks and the boundary locations of body regions in the cranio-caudal direction is then learned through a neural network regressor, and the locations are predicted. Based on low-dose unenhanced CT images of 180 near whole-body PET/CT scans (which includes 34 whole-body PET/CT scans), the mean localization error for the boundaries of superior of thorax (TS) and inferior of thorax (TI), expressed as number of slices (slice spacing ≈ 4mm)), and using either the skeleton or the pleural spaces as reference objects, is found to be 3,2 (using skeleton) and 3, 5 (using pleural spaces) respectively, or in mm 13, 10 mm (using skeleton) and 10.5, 20 mm (using pleural spaces), respectively. Improvements of this performance via optimal selection of objects and virtual landmarks and other object analytics applications are currently being pursued. and the skeleton and pleural spaces used as a reference objects

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Section: Experiments and Resultsmentioning

confidence: 62%

Automatic thoracic body region localization

et al. 2017

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“…Published works directly addressing the above problem are quite sparse. [8][9][10] In fact, we did not come across any publication that directly dealt with the specific problem of identifying body regions as addressed in this paper. As to specific body region localization, in order to detect lymphoma regions automatically in the thresholded whole-body PET/CT images, Bi et al 8 used an adaptive thresholding method to estimate the section of lungs, and then partition roughly PET/ CT images into three sectionsabove lungs, lungs, and below lungs to reduce the search space.…”

Section: B Related Workmentioning

confidence: 99%

“…Published works directly addressing the above problem are quite sparse . In fact, we did not come across any publication that directly dealt with the specific problem of identifying body regions as addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Body region localization in whole‐body low‐dose CT images of PET/CT scans using virtual landmarks

et al. 2019

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Purpose Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and extremities. In this study, we present a new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition. Methods The proposed approach consists of the following steps. First, a set of reference objects is selected and roughly segmented. Virtual landmarks (VLs) for the objects are then identified by using principal component analysis and recursive subdivision of the object via the principal axes system. The VLs can be defined based on just the binary objects or objects with gray values also considered. The VLs may lie anywhere with respect to the object, inside or outside, and rarely on the object surface, and are tethered to the object. Second, a classic neural network regressor is configured to learn the geometric mapping relationship between the VLs and the boundary locations of each body region. The trained network is then used to predict the locations of the body region boundaries. In this study, we focus on three body regions — thorax, abdomen, and pelvis, and predict their superior and inferior axial locations denoted by TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively, for any given volume image I. Two kinds of reference objects — the skeleton and the lungs and airways, are employed to test the localization performance of the proposed approach. Results Our method is tested by using low‐dose unenhanced computed tomography (CT) images of 180 near whole‐body 18F‐fluorodeoxyglucose‐positron emission tomography/computed tomography (FDG‐PET/CT) scans (including 34 whole‐body scans) which are randomly divided into training and testing sets with a ratio of 85%:15%. The procedure is repeated six times and three times for the case of lungs and skeleton, respectively, with different divisions of the entire data set at this proportion. For the case of using skeleton as a reference object, the overall mean localization error for the six locations expressed as number of slices (nS) and distance (dS) in mm, is found to be nS: 3.4, 4.7, 4.1, 5.2, 5.2, and 3.9; dS: 13.4, 18.9, 16.5, 20.8, 20.8, and 15.5 mm for binary objects; nS: 4.1, 5.7, 4.3, 5.9, 5.9, and 4.0; dS: 16.2, 22.7, 17.2, 23.7, 23.7, and 16.1 mm for gray objects, respectively. For the case of using lungs and airways as a reference object, the corresponding results are, nS: 4.0, 5.3, 4.1, 6.9, 6.9, and 7.4; dS: 15.0, 19.7, 15.3, 26.2, 26.2, and 27.9 mm for binary objects; nS: 3.9, 5.4, 3.6, 7.2, 7.2, and 7.6; dS: 14.6, 20.1, 13.7, 27.3, 27.3, and 28.6 mm for gray objects, respectively. Conclusions Precise body region identification automatically in whole‐body or body region tomographic images is vital for numerous medical image analysis and analytics applications. Despite its importance, this issue has received very little attention in the literature. We present a solution to this problem in this study using the conce...

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“…However, the method is not yet tested on adipose tissues, let alone for separately quantifying VAT and SAT. A recent study presented by Hussein et al 41 proposed a fully automatic method to segment SAT and VAT on low-dose CT images. They firstly utilized geometric median absolute deviation and local outlier scores to remove the outlier point.…”

Section: B Related Workmentioning

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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Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

Cited by 24 publications

References 49 publications

Automatic thoracic body region localization

Automatic thoracic body region localization

Body region localization in whole‐body low‐dose CT images of PET/CT scans using virtual landmarks

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

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