# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
## 7 Conclusions  
In this work, we have outlined a method for prostate segmentation which incorporates a prostate contour model, active shape probabilistic model of the prostate, a superpixel image processing algorithm, and graph-cut-based contour optimization. The proposed contour model was validated independently of the prostate segmentation algorithm by comparing the mean-squared error and maximum absolute error between input, clinician-segmented, prostate contours and the resulting contour models after parameter fitting. Two sets of 100 3D clinician-segmented prostate contours were used for validation, with one set consisting of 3D contours containing 7 to 13 2D TRUS image-derived contours of the prostate and the second set having 3D contours with each containing 7 to 16 2D contours segmented using MR images of the prostate. The results of this validation, given in Table 1, show that the clinician-segmented contours, from either imaging modality, can be represented in the contour model with sub-millimetre accuracy on average. The average mean-squared error across the two input contour sets was 0.61 (\(\pm\)0.16) mm and the average maximum absolute error was 1.54 (\(\pm\)0.31) mm. The largest absolute error observed in parameter fitting of all 200 contours was 2.12 mm. With these results, the prostate contour model was shown to be unaffected by imaging modality and capable of modelling a variety of prostate shapes with an acceptable accuracy. The prostate contour model was then incorporated, using interpolation between Gaussian normal distribution models, into an active shape statistical contour model of the prostate. This statistical shape model describes the probability of the edge of the prostate being at a particular location in a TRUS image slice. The CDF for the statistical shape model was incorporated as a parameter within the graph-cutting cost function to utilize prior prostate contour information for algorithmic prostate segmentation. The graph to be segmented was output by a novel superpixel image processing algorithm which uses a hierarchical structure to simultaneously optimize pixel clusters, with respect to pixel intensity and Euclidean-based distance metrics, and superpixel regions, which are composed of a number of pixel clusters. These superpixel regions separate an input image into areas with similar image statistics, in a type of edge and texture discrimination, with the statistical similarity between two superpixel regions being another parameter within the graph-cutting cost function. The superpixel image processing algorithm created and updated a graph structure, with the superpixel regions corresponding to the vertices of the graph and neighbouring superpixel regions being interconnected by a graph edge. Graph-cuts are performed on the resulting superpixel adjacency graph, after image processing, in order to segment out superpixel regions which are inside of the prostate in the TRUS image. This prostate segmentation algorithm was tested on nine prostate TRUS image sets, consisting of six or seven prostate image slices, to assess the algorithmic segmentation accuracy. The output of the prostate segmentation algorithm was compared to prostate segmentation performed by a clinician, in those TRUS image sets, with an average mean absolute difference of 2.52 (\(\pm\)1.66) mm and an average maximum absolute difference of 7.19 (\(\pm\)1.22) mm. The average Jaccard index of the algorithm was 0.79 (\(\pm\)0.07). The average Jaccard index result is satisfactory for the limited number of TRUS image sets that were tested; however, the continuation of this work will require a significantly larger number of the TRUS prostate image sets to fine-tune the optimization parameters used during superpixel image processing and segmentation. All authors contributed to paper conceptualization; paper preparation and experimentation was performed by J.C.; paper review and editing, R.S., N.U., and M.T. All authors have read and agreed to the published version of the manuscript.