Partitioned edge-function-scaled region-based active contour (p-ESRAC): automated liver segmentation in multiphase
Jihun Oh1, Diego R Martin2, Xiaoping Hu3
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332.
Purpose:
In multiphase contrast-enhanced magnetic resonance imaging (CE-MRI), liver segmentation is an important preprocessing step for the computer-aided evaluation of liver disease. However, because of the liver's irregular shape, proximity to surrounding organs, and large intensity variation, and peripheral contrast enhancement in the kidney, liver segmentation has been very challenging. This paper presents a novel hybrid active contour model and overall procedures that are specific to liver segmentation.
Methods:
The authors introduced an edge-function-scaled (weighted) region-based active contour model (ESRAC) and utilization of registered, multiphase sequences to address leakage-to-kidney and oversegmentation problems. The model incorporated weighted regional information with a compactly supported edge map, computed from a combination of images obtained during arterial and delayed phases, and it was coupled with a geodesic edge term. To cope with signal-inhomogeneity on MRI, all of the axial slices were partitioned into eight sectors with an angular span of 45°, centered on the inferior vena cava, each in the superior and inferior regions and the regional information regarding ESRAC was computed for each partition, henceforth the so-called partitioned ESRAC (p-ESRAC). Initialization of the active contour was performed by thresholding with a range of [200, +∞) and simple morphological operation during the delayed phase. At the end, to fill the holes in the segmented images caused by high gradients around the vasculature, noise, or outstanding texture features, iterative morphological operations were performed until convergence. The authors compared the segmentation accuracy of p-ESRAC to that with geodesic active contour, region-based active contour, geodesic active region, and localized region-based active contour using quantitative and visual assessments.
Results:
In three-dimensional experimental studies conducted on 30 subjects (14 normal or benign cases and 16 malignant cases), compared with other active contours, p-ESRAC achieved the highest dice similarity coefficients of 93.9 ± 1.6% (normal) and 91.6 ± 2.2% (malignant), respectively. In addition, p-ESRAC resulted in the lowest false positive rates of 4.5 ± 3.2% (normal) and 7.9 ± 3.0% (malignant), demonstrating it to be the most effective in reducing oversegmentation. The partition scheme improved segmentation accuracy by 5.4 ± 9.2% (normal) and 22.2 ± 27.6% (malignant) of the true segmentation that was missed by ESRAC. Visual assessment confirmed that p-ESRAC prevented leakage of the segmentation results of the liver into the kidney.
Conclusions:
A novel active-contour model was developed, allowing for accurate liver segmentation on multiphase CE-MRI, with conditions that include signal inhomogeneity and weak boundary conditions. Such a technique could be useful for applications that involve computer-aided diagnosis of liver disease.
