Abstract
Magnetic Resonance Elastography (MRE) is an MRI-based technique that is used for the clinical diagnosis and staging of liver fibrosis by quantitatively measuring the stiffness of the liver. Due to the complexity of the signal characteristics and the presence of artifacts both in the acquired images and in the resulting stiffness images, the selection of the ROI for the stiffness measurement is currently performed manually, which may lead to significant inter- and intrareader variability. An algorithm has been developed to fully automate this analysis for liver MRE images. Automated segmentation of liver MRE images is challenging due to signal inhomogeneity, low contrast, and variability in patient anatomy. An initial liver contour is found by fitting Gaussian peaks to the image histogram and selecting the peak that comprises intensities in the expected range and produces a mask near the expected location of the liver. After correction to reduce intensity inhomogeneity, an active contour based on intensity, with morphology used to implicitly enforce smoothness, is used to segment liver tissue while avoiding blood vessels. The resulting mask is used to initialize another segmentation which splits the region of the elastogram belonging to the liver into homogeneous liver tissue and areas with inclusions, partial volume effects, and artifacts. In a set of 88 cases the algorithm had a -6.0 ± 14.2% stiffness difference from an experienced reader, which was superior to the 6.8 ± 22.8% difference between two readers. The segmentation was run on an additional 200 cases and the final ROIs were subjectively rated by a radiologist. The ROIs in 98% of cases received an average rating of "good" or "acceptable.
| Original language | English (US) |
|---|---|
| Title of host publication | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Volume | 8672 |
| DOIs | |
| State | Published - 2013 |
| Event | Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging - Lake Buena Vista, FL, United States Duration: Feb 10 2013 → Feb 13 2013 |
Other
| Other | Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging |
|---|---|
| Country | United States |
| City | Lake Buena Vista, FL |
| Period | 2/10/13 → 2/13/13 |
Fingerprint
Keywords
- Automation
- Hepatic fibrosis
- Liver
- Mr elastography
- Segmentation
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Radiology Nuclear Medicine and imaging
Cite this
Stable automated segmentation of liver MR elastography images for clinical stiffness measurement. / Dzyubak, Bogdan; Venkatesh, Sudhakar K; Glaser, Kevin; Yin, Meng; Talwalkar, Jayant; Chen, Jun; Manduca, Armando; Ehman, Richard Lorne.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8672 2013. 86721I.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Stable automated segmentation of liver MR elastography images for clinical stiffness measurement
AU - Dzyubak, Bogdan
AU - Venkatesh, Sudhakar K
AU - Glaser, Kevin
AU - Yin, Meng
AU - Talwalkar, Jayant
AU - Chen, Jun
AU - Manduca, Armando
AU - Ehman, Richard Lorne
PY - 2013
Y1 - 2013
N2 - Magnetic Resonance Elastography (MRE) is an MRI-based technique that is used for the clinical diagnosis and staging of liver fibrosis by quantitatively measuring the stiffness of the liver. Due to the complexity of the signal characteristics and the presence of artifacts both in the acquired images and in the resulting stiffness images, the selection of the ROI for the stiffness measurement is currently performed manually, which may lead to significant inter- and intrareader variability. An algorithm has been developed to fully automate this analysis for liver MRE images. Automated segmentation of liver MRE images is challenging due to signal inhomogeneity, low contrast, and variability in patient anatomy. An initial liver contour is found by fitting Gaussian peaks to the image histogram and selecting the peak that comprises intensities in the expected range and produces a mask near the expected location of the liver. After correction to reduce intensity inhomogeneity, an active contour based on intensity, with morphology used to implicitly enforce smoothness, is used to segment liver tissue while avoiding blood vessels. The resulting mask is used to initialize another segmentation which splits the region of the elastogram belonging to the liver into homogeneous liver tissue and areas with inclusions, partial volume effects, and artifacts. In a set of 88 cases the algorithm had a -6.0 ± 14.2% stiffness difference from an experienced reader, which was superior to the 6.8 ± 22.8% difference between two readers. The segmentation was run on an additional 200 cases and the final ROIs were subjectively rated by a radiologist. The ROIs in 98% of cases received an average rating of "good" or "acceptable.
AB - Magnetic Resonance Elastography (MRE) is an MRI-based technique that is used for the clinical diagnosis and staging of liver fibrosis by quantitatively measuring the stiffness of the liver. Due to the complexity of the signal characteristics and the presence of artifacts both in the acquired images and in the resulting stiffness images, the selection of the ROI for the stiffness measurement is currently performed manually, which may lead to significant inter- and intrareader variability. An algorithm has been developed to fully automate this analysis for liver MRE images. Automated segmentation of liver MRE images is challenging due to signal inhomogeneity, low contrast, and variability in patient anatomy. An initial liver contour is found by fitting Gaussian peaks to the image histogram and selecting the peak that comprises intensities in the expected range and produces a mask near the expected location of the liver. After correction to reduce intensity inhomogeneity, an active contour based on intensity, with morphology used to implicitly enforce smoothness, is used to segment liver tissue while avoiding blood vessels. The resulting mask is used to initialize another segmentation which splits the region of the elastogram belonging to the liver into homogeneous liver tissue and areas with inclusions, partial volume effects, and artifacts. In a set of 88 cases the algorithm had a -6.0 ± 14.2% stiffness difference from an experienced reader, which was superior to the 6.8 ± 22.8% difference between two readers. The segmentation was run on an additional 200 cases and the final ROIs were subjectively rated by a radiologist. The ROIs in 98% of cases received an average rating of "good" or "acceptable.
KW - Automation
KW - Hepatic fibrosis
KW - Liver
KW - Mr elastography
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=84878273898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878273898&partnerID=8YFLogxK
U2 - 10.1117/12.2006943
DO - 10.1117/12.2006943
M3 - Conference contribution
AN - SCOPUS:84878273898
SN - 9780819494467
VL - 8672
BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
ER -