# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
## 1 Introduction  
Prostate brachytherapy is a procedure to treat prostate cancer where long flexible needles, containing radioactive seeds, are inserted into the prostate. This procedure is an attractive treatment option used in early-stage locally-confined prostate cancers [1, 2]. In current practice, prostate brachytherapy requires a planning stage where the entire volume of the prostate is imaged and the 3D contour of the prostate is created. The 3D prostate contour is used for dosimetric calculation and, traditionally, is manually segmented by a clinician in software. While either MR or CT imaging modalities can be used for the dosimetric planning, the typical imaging modality used is ultrasound, both for cost-effectiveness and to match the imaging modality used by the clinician during the insertion procedure. This manual segmentation of the prostate from axial image slices is time-consuming, and thus algorithmic prostate segmentation poses an attractive option to reduce the amount of time that a clinician is required during prostate brachytherapy preplanning. In addition to therapeutic intervention planning, prostate gland contours can also be used for medical diagnostics, through measuring parameters such as gland asymmetry [3] or gland image appearance [4].  
To assist a clinician when segmenting the prostate, we propose a fully-autonomous segmentation algorithm, shown in Figure 1, that consists of three main parts: a statistics-based active shape model, superpixel image clustering, and graph-cut-based segmentation. First, a 3D prostate contour model is defined that will be used to model, and later segment,the shape of the prostate in TRUS images. Utilizing a dataset of previously gathered manually segmented prostate contours, a Gaussian-distribution-based statistical model of prostate shapes will be created. This Gaussian distribution model thus forms the active shape prostate model and is used to estimate the range of values (and corresponding probability) for each parameter in the shape model. With the active shape model found, the next step in the prostate segmentation algorithm involves the image processing of a full set of axial images for a patient (capturing the 3D shape of the prostate). A novel superpixel algorithm is used on each TRUS image in the set to cluster contiguous pixels together, based on statistical similarity, into large regions of superpixels. By utilizing the statistical dissimilarity between neighbouring superpixel regions after processing, the edges of the prostate in each axial image can be found.  
The final 3D prostate contour segmentation is carried out through a graph-cut based method, to find the most likely prostate contour based on the active shape model and informed by edge features detected by the superpixel algorithm. This algorithm is thus a hybrid method which incorporates information from an active shape model into a graph-cut based segmentation system. The work of [5] proposed a similar style of hybrid system, where an active contour model is progressively moved and deformed through graph-cut-based optimization. This hybrid method was shown to be robust to image noise and large edge discontinuities. This robustness to edge discontinuities is of particular interest in medical imaging, where [6] applied a similar hybrid active contour graph-cut model, and used a series of graph-cut-based expansion and contraction steps to move the contour and segment out a kidney in CT image slices. In a similar vein to our proposed method, ref. [7] proposed augmenting an active shape-active appearance model (incorporating both contour and image statistics for the organ of interest) with a graph-cut-based optimization to delineate 3D contours of livers, kidneys, and spleens in CT image volumes. This hybrid segmentation methodology has the significant advantage that it can match the \"contouring style\" of a particular clinician and is significantly robust to the poor contrast and prostate edge definition seen in TRUS prostate images.