Superpixel-Based Segmentation for 3D Prostate MR Images

Abstract

This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 ±1.9%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0±3.2%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods.

Fig. 1

The flowchart of the proposed segmentation method.

Fig. 2

Demonstration of the original MR images (top) and the corresponding superpixels (middle) for the slices at the mid-gland (left), base (middle), and apex (right) of the prostate. The region around the prostate is magnified to show the superpixels (bottom). Each MR image contains about 1,000 superpixels.

Fig. 3

The 3D superpixel-based neighborhood system in the 3D graph cuts algorithm. The red solid lines are inter-edges, while the blue dotted lines are intra-edges. The dash lines are terminal edges [24], l is the label of the superpixel. Label 1 corresponds to the prostate, while Label 0 represents the background.

Fig. 4

The framework of the 3D graph cuts algorithm.

Fig. 5

The superpixel-based shape data terms. Left: the original slice. Middle: the superpixel-based shape data term of the prostate. Right: the superpixel-based shape data term of the background. The penalties of the shape features will be used when the superpixels are assigned as the prostate or background. Blue color represents a low penalty, while red represents a high penalty.

Fig. 6

On the left graph, the blue solid curves are prostate contours in the three key slices selected at the base, mid-gland and apex of the prostate. The blue dots are manually labelled markers. The red dotted lines are the true boundary of the prostate. The red dots on the red dotted lines are obtained using the interpolation of the corresponding blue dots in the 3D space. On the right graph, the red solid curve is the core shape, which is fitted based on the interpolated red dots.

Fig. 7

Qualitative evaluation of the prostate segmentation on six MR volumes. We choose the volumes every seventh case. The blue curves are the manually labeled ground truth, while the red curves are the segmentations of the proposed method for the apex (left), mid-gland (middle), and base (right) of the prostate. The values of DSC(%), RVD(%), HD(mm), and ASD(mm) of each case are overlaid on the images.

Fig. 8

3D visualization of the segmented prostate (white regions) compared to the manual ground truth (gold regions) in two different views.

Fig. 9

The effect of the number of the points on the segmentation performance. When the number of the points varies from 4 to 20, the DSC has very small changes. The proposed method is insensitive to the number of mark points picked by the user.

Fig. 10

The effect of selecting three key slices on the segmentation performance. Five successive slices around each key slice are chosen to be the key slices individually. The mean DSC and standard deviation are from the 43 MR volumes.

Fig. 11

The effect of superpixel size on the segmentation performance. Five sizes of superpixel are chosen to test the ability of the segmentation method, which are 50, 70, 100, 150 and 200 pixels.

Fig. 12

Comparison of three segmentation methods: graph cuts only, active contour model only, and the hybrid approach of the two methods.

Fig. 13

Prostate segmentation of two adjacent slices at the bladder neck (Blue: the ground truth from manual segmentation. Red: the segmentation by the algorithm).