SYSTEM AND METHOD FOR IMAGE-BASED QUANTIFICATION OF WHITE AND BROWN ADIPOSE TISSUE AT THE WHOLE-BODY, ORGAN AND BODY-REGION LEVELS

• US 20180165808A1
• Filed: 06/27/2017
• Published: 06/14/2018
• Est. Priority Date: 06/27/2016
• Status: Active Grant

• Current Assignee: University of Central Florida Research Foundation Inc. (State University System of Florida)
• Original Assignee: University of Central Florida Research Foundation Inc. (State University System of Florida)
• Inventors: Bagci, Ulas, Hussein, Sarfaraz
• Granted Patent: US 10,157,462 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/50 specially adapted for specific body parts; specially adapted for specific clinical applications
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10104 Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/20156 Automatic seed setting
• CPC Class Codes: G06T 2207/30024 Cell structures in vitro; Tissue sections in vitro
• CPC Class Codes: G06T 7/00 Image analysis
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/12 Edge-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 : using neural networks
• CPC Class Codes: G06V 2201/031 : of internal organs
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

A system and method for automatically detecting and quantifying adiposity distribution is presented herein. The system detects, segments and quantifies white and brown fat adipose tissues at the whole-body, body region, and organ levels.

Claims

1. A method of automatically detecting and quantifying white and brown adipose tissue from an imaging scan of a subject comprising:

providing the imaging scan of the subject;

automatically detecting a body region of the subject in the imaging scan;

separating and segmenting subcutaneous adipose tissue (SAT) from visceral adipose tissue (VAT) in the imaging scan of the subject; and

detecting and segmenting brown adipose tissue (BAT) from other tissue in the imaging scan of the subject.

2. The method of claim 1, wherein the body region detected is selected from the group consisting of an abdominal region and a thorax region.

3. The method of claim 2, wherein the body region is automatically detected by using a detection algorithm based on deep learning features.

4. The method of claim 1, wherein the separating and segmenting of SAT from VAT step further comprising:

segmenting total adipose tissue (TAT) in the imaging scan;

estimating an initial boundary between SAT and VAT in the abdominal region;

removing outliers from the boundary; and

fusing labels of boundary candidates across different slices and creating a fine SAT-VAT separating surface.

5. The method of claim 4, wherein the outliers are removed from the boundary using geometric median absolute derivation (MAD) and local outlier scores (LoOS).

6. The method of claim 4, wherein the SAT-VAT separating surface is created using 3D Conditional Random Fields (CRF) using shape, anatomy and appearance cues.

7. The method of claim 1, wherein the detecting and segmenting of BAT step further comprises:

identifying total adipose tissue (TAT) in the imaging scan;

performing automatic seed selection for BAT;

delineating potential BAT regions; and

differentiating BAT regions from non-BAT regions.

8. The method of claim 7, wherein fixed Hounsfield unit (HU) interval filtering is used to identify TAT.

9. The method of claim 7, wherein background and foreground seeds are identified during automatic seed selection.

10. The method of claim 7, wherein image co-segmentation using Random Walk (RW) is used to delineate potential BAT regions.

11. The method of claim 7, wherein a probabilistic metric based on a combination of total variation and Cramer-Von Mises distances is used to differentiate BAT regions from non-BAT regions.

12. The method of claim 1, further comprising automatically detecting specific organs containing adipose tissue comprising:

extracting 3D convolutional neural network (CNN) features from source data;

transforming 3D CNN features from source data to target data by applying Geodesic Flow Kernal (GFK) to the 3D CNN features; and

localizing the organ in a bounding volume using Random Forest;

wherein the target data is organ detection in 3D CT scans.

13. The method of claim 1, wherein the imaging scan is selected from the group consisting of a positron emission tomography/computed tomography (PET/CT) scan, a positron emission tomography/magnetic resonance imaging scan (PET/MRI) and a contrast-enhanced ultrasound (CEUS) scan.

14. A method of creating a risk profile of a subject by automatically detecting and quantifying white and brown adipose tissue from an imaging scan of the subject comprising:

providing the imaging scan of the subject;

automatically detecting a body region of the subject in the imaging scan wherein the body region detected is an abdominal region or a thorax region;

separating and segmenting subcutaneous adipose tissue (SAT) from visceral adipose tissue (VAT) in the imaging scan of the subject;

detecting and segmenting brown adipose tissue (BAT) from other tissue in the imaging scan of the subject; and

creating a risk profile based on the quantitative amount of VAT and BAT found in the subject.

15. The method of claim 14, wherein the separating and segmenting subcutaneous adipose tissue (SAT) from visceral adipose tissue (VAT) in the imaging scan step further comprising:

automatically detecting white adipose tissue (WAT) in the imaging scan;

segmenting total adipose tissue (TAT);

estimating a SAT-VAT separation boundary;

removing outliers using geometric median absolute derivation (MAD) and local outlier scores (LoOS); and

fusing labels of boundary candidates across different slices of an image volume to create a fine SAT-VAT separating surface using 3D Conditional Random Fields (CRF) using shape, anatomy and appearance cues.

16. The method of claim 14, wherein the detecting and segmenting brown adipose tissue (BAT) from other tissue step further comprising:

identifying TAT in the imaging scan;

performing automatic seed selection for BAT;

performing image co-segmentation; and

differentiating BAT regions from non-BAT regions.

17. The method of claim 14, further comprising automatically detecting specific organs containing adipose tissue comprising:

extracting 3D convolutional neural network (CNN) features from source data;

transforming 3D CNN features from source data to target data by applying Geodesic Flow Kernal (GFK) to the 3D CNN features; and

localizing the organ in a bounding volume using Random Forest;

wherein the target data is organ detection in 3D CT scans.

18. The method of claim 14, wherein the imaging scan is selected from the group consisting of a positron emission tomography/computed tomography (PET/CT) scan, a positron emission tomography/magnetic resonance imaging scan (PET/MRI) and a contrast-enhanced ultrasound (CEUS) scan.

19. A method of automatically detecting and quantifying white and brown adipose tissue from an imaging scan of a subject comprising:

providing the imaging scan of the subject wherein the imaging scan is a positron emission tomography/computed tomography (PET/CT) scan;

automatically detecting a body region of the subject in the imaging scan;

separating and segmenting subcutaneous adipose tissue (SAT) from visceral adipose tissue (VAT) in the imaging scan of the subject comprising;

segmenting total adipose tissue (TAT);

estimating a SAT-VAT separation boundary;

removing outliers using geometric median absolute derivation (MAD) and local outlier scores (LoOS); and

fusing labels of boundary candidates across different slices of an image volume to create a fine SAT-VAT separating surface;

detecting and segmenting brown adipose tissue (BAT) from other tissue in the imaging scan of the subject comprising;

identifying TAT in the imaging scan;

performing automatic seed selection for BAT;

performing image co-segmentation; and

differentiating BAT regions from non-BAT regions.

20. The method of claim 19, further comprising automatically detecting specific organs containing adipose tissue comprising:

extracting 3D convolutional neural network (CNN) features from source data;

transforming 3D CNN features from source data to target data by applying Geodesic Flow Kernal (GFK) to the 3D CNN features; and

localizing the organ in a bounding volume using Random Forest;

wherein the target data is organ detection in 3D CT scans.