CAVASS: a framework for medical imaging applications

Grevera, George J.; Udupa, Jayaram K.; Odhner, Dewey

doi:10.1117/12.851180

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…Delineation results are shown in Figure 3 for Skin, SAT and VAT overlaid on the original gray images. In figure 4, the delineated result of SAT from IRFC is shown via volume rendering with 3DVIEWNIX and CAVASS software [7]. Delineation accuracies in terms of true positive (TP) and false positive (FP) volume fractions [8] are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition

et al. 2013

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In studying Obstructive Sleep Apnea Syndrome (OSAS) in obese children, the quantification of obesity through MRI has been shown to be useful. For large-scale studies, interactive or manual segmentation strategies become inadequate. Our goal is to automate this process to facilitate high throughput, precision, and accuracy and to eliminate subjectivity in quantification. In this paper, we demonstrate the adaptation, to this application, of a general body-wide Automatic Anatomy Recognition (AAR) system that is being developed separately. The AAR system has been developed based on existing clinical CT image data of 50-60 year-old male subjects and using fuzzy models of a large number of objects in each body region. The individual objects and their models are arranged in a hierarchy that is specific to each body region. In the application under consideration in this paper, we are primarily interested in only the skin boundary, and subcutaneous and visceral adipose region. Further, the image modality is MRI, and the study subjects are 8-17 year-old females. We demonstrate in this paper that, once such a full AAR system is built, it can be easily adapted to a new application by specifying the objects of interest, their hierarchy, and a few other application-specific parameters. Our tests based on MRI of 14 obese subjects indicate a recognition accuracy of about 2 voxels or better for both types of adipose regions. This seems quite adequate in terms of the initialization of model-based graph-cut (GC) and iterative relative fuzzy connectedness (IRFC) algorithms implemented in our AAR system for subsequent delineation of the objects. Both algorithms achieved low false positive volume fraction (FPVF) and high true positive volume fraction (TPVF), with IRFC performing better than GC.

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Section: Resultsmentioning

confidence: 99%

Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition

et al. 2013

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“…We first segment the vertebral column in I s , create 3D surface renditions of the column, and interactively indicate in this display the axial locations. We use our visualization software package 24 for selecting locations quantitatively precisely on shaded surface renditions. In MR images, however, this approach will be more difficult since segmentation of the vertebral column is challenging.…”

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confidence: 99%

Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space: A novel approach

2014

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Purpose: The quantification of body fat plays an important role in the study of numerous diseases. It is common current practice to use the fat area at a single abdominal computed tomography (CT) slice as a marker of the body fat content in studying various disease processes. This paper sets out to answer three questions related to this issue which have not been addressed in the literature. At what single anatomic slice location do the areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) estimated from the slice correlate maximally with the corresponding fat volume measures? How does one ensure that the slices used for correlation calculation from different subjects are at the same anatomic location? Are there combinations of multiple slices (not necessarily contiguous) whose area sum correlates better with volume than does single slice area with volume? Methods: The authors propose a novel strategy for mapping slice locations to a standardized anatomic space so that same anatomic slice locations are identified in different subjects. The authors then study the volume-to-area correlations and determine where they become maximal. To address the third issue, the authors carry out similar correlation studies by utilizing two and three slices for calculating area sum. Results: Based on 50 abdominal CT data sets, the proposed mapping achieves significantly improved consistency of anatomic localization compared to current practice. Maximum correlations are achieved at different anatomic locations for SAT and VAT which are both different from the L4-L5 junction commonly utilized currently for single slice area estimation as a marker. Conclusions: The maximum area-to-volume correlation achieved is quite high, suggesting that it may be reasonable to estimate body fat by measuring the area of fat from a single anatomic slice at the site of maximum correlation and use this as a marker. The site of maximum correlation is not at L4-L5 as commonly assumed, but is more superiorly located at T12-L1 for SAT and at L3-L4 for VAT. Furthermore, the optimal anatomic locations for SAT and VAT estimation are not the same, contrary to common assumption. The proposed standardized space mapping achieves high consistency of anatomic localization by accurately managing nonlinearities in the relationships among landmarks. Multiple slices achieve greater improvement in correlation for VAT than for SAT. The optimal locations in the case of multiple slices are not contiguous.

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Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

et al. 2013

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CAVASS: a framework for medical imaging applications

Cited by 3 publications

References 47 publications

Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition

Abdominal adiposity quantification at MRI via fuzzy model-based anatomy recognition

Optimization of abdominal fat quantification on CT imaging through use of standardized anatomic space: A novel approach

Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

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