Assisted quantification of abdominal adipose tissue based on magnetic resonance images Article uri icon

abstract

  • An assisted method to segment Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT) from Magnetic Resonance Imaging (MRI) slices is presented. The segmentation process, called shape-based segmentation, consists in three main steps: 1) to draw a series of closed curves at different slices that separates the abdominal structures of interest, 2) to generate a 3D model from the closed curves for each abdominal structure by using shape-based interpolation and 3) to apply a segmentation algorithm to define the adipose tissue. The 3D models considerably simplify the problem since the abdominal structures are separated, and in turn, this reduces the possibility of large segmentation errors. In addition, a fully automatic segmentation procedure was also implemented. Twenty slices of MRI at the abdominal region for each of twelve subjects were analysed. The results of the shape-based and automatic segmentation were compared with the expert segmentation carried out in the slice located at the umbilicus level. Correlation Coefficient (CC) and volume error (VE) were used as performance measures. The comparison between the expert and shape-based segmentation for SAT yielded results of CC= 0.974 and VE=-0.01 ± 5.8 cm3, while for VAT the performance indexes were CC= 0.993 and VE= 0.9 ± 1.8 cm3. The results suggest that the shape-based segmentation provides an accurate and simple assessment of the abdominal adiposity with minimal human intervention and it could be used as a simple tool in clinics. © 2019, Springer Science Business Media, LLC, part of Springer Nature.
  • An assisted method to segment Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT) from Magnetic Resonance Imaging (MRI) slices is presented. The segmentation process, called shape-based segmentation, consists in three main steps: 1) to draw a series of closed curves at different slices that separates the abdominal structures of interest, 2) to generate a 3D model from the closed curves for each abdominal structure by using shape-based interpolation and 3) to apply a segmentation algorithm to define the adipose tissue. The 3D models considerably simplify the problem since the abdominal structures are separated, and in turn, this reduces the possibility of large segmentation errors. In addition, a fully automatic segmentation procedure was also implemented. Twenty slices of MRI at the abdominal region for each of twelve subjects were analysed. The results of the shape-based and automatic segmentation were compared with the expert segmentation carried out in the slice located at the umbilicus level. Correlation Coefficient (CC) and volume error (VE) were used as performance measures. The comparison between the expert and shape-based segmentation for SAT yielded results of CC= 0.974 and VE=-0.01 ± 5.8 cm3, while for VAT the performance indexes were CC= 0.993 and VE= 0.9 ± 1.8 cm3. The results suggest that the shape-based segmentation provides an accurate and simple assessment of the abdominal adiposity with minimal human intervention and it could be used as a simple tool in clinics. © 2019, Springer Science%2bBusiness Media, LLC, part of Springer Nature.

publication date

  • 2020-01-01