Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

Anders Nymark Christensen; Christian Thode Larsen; Camilla Maria Mandrup Jensen; Martin Bæk Petersen; Rasmus Larsen; Knut Conradsen; Vedrana Andersen Dahl

doi:10.1007/978-3-319-59129-2_10

Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

Anders Nymark Christensen, Christian Thode Larsen, Camilla Maria Mandrup Jensen, Martin Bæk Petersen, Rasmus Larsen, Knut Conradsen, Vedrana Andersen Dahl

University of Copenhagen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.

Original language	English
Title of host publication	Image Analysis
Volume	10270
Publisher	Springer
Publication date	2017
Pages	109-120
ISBN (Print)	9783319591285
DOIs	https://doi.org/10.1007/978-3-319-59129-2_10
Publication status	Published - 2017
Event	20th Scandinavian Conference on Image Analysis - Tromsø, Norway Duration: 12 Jun 2017 → 14 Jun 2017 Conference number: 20 https://link.springer.com/book/10.1007/978-3-319-59126-1

Conference

Conference	20th Scandinavian Conference on Image Analysis
Number	20
Country/Territory	Norway
City	Tromsø
Period	12/06/2017 → 14/06/2017
Internet address	https://link.springer.com/book/10.1007/978-3-319-59126-1

Series	Lecture Notes in Computer Science
Volume	10270
ISSN	0302-9743

Keywords

Computer Science
Image Processing and Computer Vision
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Computer Graphics
Data Mining and Knowledge Discovery

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10.1007/978-3-319-59129-2_10

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title = "Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies",

abstract = "For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.",

keywords = "Computer Science, Image Processing and Computer Vision, Pattern Recognition, Artificial Intelligence (incl. Robotics), Computer Graphics, Data Mining and Knowledge Discovery",

author = "Christensen, {Anders Nymark} and Larsen, {Christian Thode} and {Mandrup Jensen}, {Camilla Maria} and Petersen, {Martin B{\ae}k} and Rasmus Larsen and Knut Conradsen and Dahl, {Vedrana Andersen}",

year = "2017",

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language = "English",

isbn = "9783319591285",

volume = "10270",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "109--120",

booktitle = "Image Analysis",

note = "20th Scandinavian Conference on Image Analysis, SCIA 2017 ; Conference date: 12-06-2017 Through 14-06-2017",

url = "https://link.springer.com/book/10.1007/978-3-319-59126-1",

}

Christensen, AN, Larsen, CT, Mandrup Jensen, CM, Petersen, MB, Larsen, R , Conradsen, K & Dahl, VA 2017, Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies. in Image Analysis. vol. 10270, Springer, Lecture Notes in Computer Science, vol. 10270, pp. 109-120, 20th Scandinavian Conference on Image Analysis, Tromsø, Norway, 12/06/2017. https://doi.org/10.1007/978-3-319-59129-2_10

Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies. / Christensen, Anders Nymark; Larsen, Christian Thode; Mandrup Jensen, Camilla Maria et al.
Image Analysis. Vol. 10270 Springer, 2017. p. 109-120 (Lecture Notes in Computer Science, Vol. 10270).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

AU - Christensen, Anders Nymark

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AU - Dahl, Vedrana Andersen

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N2 - For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.

AB - For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.

KW - Computer Science

KW - Image Processing and Computer Vision

KW - Pattern Recognition

KW - Artificial Intelligence (incl. Robotics)

KW - Computer Graphics

KW - Data Mining and Knowledge Discovery

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ER -

Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

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Conference

Keywords

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