# Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
## 2 Background  
There are several artifacts and limitations inherent in ultrasound images that must be compensated for in computer-aided tissue segmentation [8]. In the specific case of prostate segmentation, the well-known shadowing and edge enhancement artifacts, which obscure the edge of the prostate in US images, have motivated the use of active shape, active contour, texture-based, and other methods for segmentation. Regardless of segmentation modality, algorithmic prostate segmentation can be grouped into two general methods, semi-autonomous and fully-autonomous systems. Semi-autonomous systems require some user input for each patient in order to perform prostate segmentation. Fully-autonomous systems, in contrast, require no such user input. The field of prostate segmentation in multiple imaging modalities, including ultrasound, CT, and MRI, has been well explored in the literature, with [9; 10] providing comprehensive surveys. A more recent survey focusing on the automated diagnosis of prostate cancers based on algorithmic prostate contouring in ultrasound images is given in [11]. In active contour model segmentation, a simple contour model is deformed through some forcing function such that the contour will come to rest on the edge of the prostate. Active contour segmentation is typically semi-autonomous, requiring user input for the initial contour placement, and can be particularly sensitive to areas of low contrast or shadowing/enhancement in US images. One of the first active contour segmentation systems was presented by [12], wherein an initial user-defined 2D contour model (later extended to 3D by [13; 14]) is fit to the edge of the prostate, with the user editing the segmented contour in areas affected by US artifacts. To compensate for incomplete edge information algorithmically, ref. [15] used an active contour model which incorporates a Kalman filter to probabilistically detect the edge of the prostate around user input seed points. To avoid the instability caused by the forcing functions in standard active contour model methods, ref. [16] enforced constraints on the contour utilizing a 1D wavelet transform, and [17] used nonlinear warping of the axial image to an ellipse-based contour model. The work of [18] represented the contour of the prostate with 2D superellipses. This concept was further developed in [19], where information from a full series of these 2D axial superellipses was refined into a single 3D ellipsoid contour. To enhance the robustness of contour-based segmentation algorithms in the presence of shadowing/enhancement and low organ-to-background contrast, a statistics-based approach known as active shape modelling has been employed. In active shape segmentation, a large number of manually segmented contours (or feature points) are input into a system to derive the probability that the contour will take a particular shape (or has particular feature point locations) [20]. By incorporating both contour and image information from manually segmented contours in a hybrid method, refs. [21; 22] used non-rigid registration to warp an input US image to all of the manually segmented US images in a database. A series of warped manually-segmented images which strongly matched the input image is then used to calculate the contour statistics, with the mean of this statistic being used as the contour for segmentation. The work of [23] presented a 2D prostate contouring algorithm using weak membrane that is deformed to segment the prostate based on edge information, enhanced by an edge-preserving filter, in TRUS images. A spline-based discrete dynamic contour model was presented in [24] and was used by [25] to segment the prostate after filtering with a dyadic wavelet transform. Using the statistical inference from a series of prostate contours, ref. [26] created a point distribution model and [27] used a spherical harmonics model, which were fit to a series of user input points and then updated based on the likelihood that nearby prostate edges in the US image correspond to the statistical model.