Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans

Jin, Yinpeng; Imielinska, Celina; Laine, Andrew F.; Udupa, Jayaram K.; Shen, Wei; Heymsfield, Steven B.

doi:10.1007/978-3-540-39899-8_78

Cited by 26 publications

(28 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14,17,[34][35][36][37][38][39] Most approaches are composed of two steps: in the first step, based on the histogram a threshold between adipose and lean tissues is determined manually or automatically and, in the second step, the areas of SAT and VAT are delineated. The segmentation routine can also include data pre-processing steps like de-noising and smoothing of the MR images to facilitate segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…40 Semi-automated approaches support the user by determining a histogram-based threshold, 30,[41][42][43][44][45][46] boundary enhancement, 47 histogram-based region growing 41,47 or clustering. 40 Hybrid algorithms, which incorporate anatomical knowledge with segmentation algorithms, 32,33,38,39 have been successfully used to segment high contrast, artifact-free images. Unfortunately, the intensity variation at tissue boundaries in abdominal MR images is gradual and not sharp causing poor differentiation of tissue interfaces.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

et al. 2007

View full text Add to dashboard Cite

Objective: To examine five available software packages for the assessment of abdominal adipose tissue with magnetic resonance imaging, compare their features and assess the reliability of measurement results. Design: Feature evaluation and test-retest reliability of softwares (NIHImage, SliceOmatic, Analyze, HippoFat and EasyVision) used in manual, semi-automated or automated segmentation of abdominal adipose tissue. Subjects: A random sample of 15 obese adults with type 2 diabetes. Measurements: Axial T1-weighted spin echo images centered at vertebral bodies of L2-L3 were acquired at 1.5 T. Five software packages were evaluated (NIHImage, SliceOmatic, Analyze, HippoFat and EasyVision), comparing manual, semi-automated and automated segmentation approaches. Images were segmented into cross-sectional area (CSA), and the areas of visceral (VAT) and subcutaneous adipose tissue (SAT). Ease of learning and use and the design of the graphical user interface (GUI) were rated. Intra-observer accuracy and agreement between the software packages were calculated using intra-class correlation. Intra-class correlation coefficient was used to obtain test-retest reliability. Results: Three of the five evaluated programs offered a semi-automated technique to segment the images based on histogram values or a user-defined threshold. One software package allowed manual delineation only. One fully automated program demonstrated the drawbacks of uncritical automated processing. The semi-automated approaches reduced variability and measurement error, and improved reproducibility. There was no significant difference in the intra-observer agreement in SAT and CSA. The VAT measurements showed significantly lower test-retest reliability. There were some differences between the software packages in qualitative aspects, such as user friendliness. Conclusion: Four out of five packages provided essentially the same results with respect to the inter-and intra-rater reproducibility. Our results using SliceOmatic, Analyze or NIHImage were comparable and could be used interchangeably. Newly developed fully automated approaches should be compared to one of the examined software packages.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Different experts, or a given expert at different times, may disagree on some parts of objects' boundaries: those effects are termed inter-and intra-user variability. Although keeping the different references of a single object can be useful, in particular to analyse these variabilities [3,4], some applications require a single reference per object, which calls for a way of merging multiple references [5,6,4]. One strategy is to average the pixel distance between the borders of the multiple references [7].…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Evaluation of Segmentation Results: Introducing Confidence Maps

Restif

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

View full text Add to dashboard Cite

Abstract. We introduce a novel framework, called Confidence Maps Estimating True Segmentations (Comets), to store segmentation references for medical images, combine multiple references, and measure the discrepancy between a segmented object and a reference. The core feature is the use of efficiently encoded confidence maps, which reflect the local variations of blur and the presence of nearby objects. Local confidence values are defined from expert user input, and used to define a new discrepancy error measure, aimed to be directly interpreted quantitatively and qualitatively. We illustrate the use of this framework to compare different segmentation methods and tune a method's parameters.

show abstract

“…snake [5], level set [6][7][8][9]), active shape [10] and active appearance models [11], and stochastic methods (Markov random field [12], graph cut [13]). Hybrid methods [14] combining different existing methods were also proposed. Among segmentation methods, deformable models are still widely used in medical image analysis, especially for cardiac imaging.…”

Section: Introductionmentioning

confidence: 99%

Real-time segmentation of 4D ultrasound by Active Geometric Functions

Duan

Angelini

Homma

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Self Cite

View full text Add to dashboard Cite

Four-dimensional ultrasound based on matrix phased array transducers can capture the complex 4D cardiac motion in a complete and real-time fashion. However, the large amount of information residing in 4D ultrasound scans and novel applications under interventional settings pose a big challenge in efficiency for workflow and computer-aided diagnostic algorithms such as segmentation. In this context, a novel formulation framework of the minimal surface problem, called Active Geometric Functions (AGF), is proposed to reach truly real-time performance in segmenting 4D ultrasound data. A specific instance of AGF based on finite element modeling and Hermite surface descriptors was implemented and evaluated on 35 4D ultrasound data sets with a total of 425 time frames. Quantitative comparison to manual tracing showed that the proposed method provides LV contours close to manual segmentation and that the discrepancy was comparable to inter-observer tracing variability. The ability of such realtime segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

show abstract

Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans

Cited by 26 publications

References 16 publications

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

Revisiting the Evaluation of Segmentation Results: Introducing Confidence Maps

Real-time segmentation of 4D ultrasound by Active Geometric Functions

Contact Info

Product

Resources

About