KEYWORDS: Image segmentation, Prostate, Ultrasonography, Signal to noise ratio, Data modeling, Binary data, Statistical modeling, Image processing, Image processing algorithms and systems, Medical imaging
Prostate segmentation in ultrasound images is a clinically important and technically challenging task. Despite several research attempts, few effective methods are available. One problem is the limited algorithmic robustness to common artifacts in clinical data sets. To improve the robustness, we have developed a hybrid level set method, which incorporates shape constraints into a region-based curve evolution process. The online segmentation method alternates between two steps, namely, shape model estimation (ME) and curve evolution (CE). The prior shape information is encoded in an implicit parametric model derived offline from manually outlined training data. Utilizing this prior shape information, the ME step tries to compute the maximum a posteriori estimate of the model parameters. The estimated shape is then used to guide the CE step, which in turn provides a new model initialization for the ME step. The process stops automatically when the curve locks onto the specific prostate shape. The ME and the CE steps complement each other to capture both global and local shape details. With shape guidance, this algorithm is less sensitive to initial contour placement and more robust even in the presence of large boundary gaps and strong clutter. Promising results are demonstrated on both synthetic and real prostate ultrasound images.
Cancer management using positron emission tomography (PET) imaging is rapidly expanding its role in clinical practice. The high sensitivity of PET to locate cancer can be confounded by the minimal anatomical information it provides. Additional anatomical information would greatly benefit diagnosis, staging, therapy planning and treatment monitoring. Computed tomography (CT) provides detailed anatomical information but is less sensitive towards cancer localization than PET. Combining PET and CT images would enable accurate localization of the functional information with respect to detailed patient anatomy. We have developed a software platform to facilitate efficient visualization of PET/CT image studies. We used a deformable registration algorithm using mutual information and a B-spline model of the deformation. Several useful visualization modes were implemented with an efficient and robust method for switching between modes and handling large datasets. Processing of several studies can be queued and the results browsed. The software has been validated with clinical data.
Automated prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing edge segments, and complex prostate peripheral anatomy. In this paper, a Bayesian prostate segmentation algorithm is presented. It combines both prior shape and image information for robust segmentation. In this study, the prostate shape was efficiently modeled using deformable superellipse. A flexible graphical user interface has been developed to facilitate the validation of our algorithm in a clinical setting. This algorithm was applied to 66 ultrasound images collected from 8 patients. The resulting mean error between the computer-generated boundaries and the manually-outlined boundaries was 1.39 ± 0.60 mm, which is significantly less than the variability between human experts.
Brachytherapy of prostate cancer involves permanent implantation of radioactive seeds in the prostate gland under transrectal ultrasound (TRUS) and fluoroscopic guidance with the goal to treat the entire gland to a prescribed tumoricidal dose. The seed coordinates are calculated prior to the procedure. However, due to uncertainties associated with the seed insertion process and changes in the anatomy, it is difficult to faithfully reproduce the planned dose distribution. The procedure can be improved substantially by identifying the underdosed regions via real-time dosimetry before the completion of implantation. The physician can then insert additional seeds to achieve clinically needed dose distribution. One possible method of performing such dosimetry is to fuse TRUS (which images the prostate gland very well but not the metallic seeds) and fluoroscopy (which images metallic seeds well but not soft tissue) data. We have developed a technique to register the 3D seed positions reconstructed from multiple fluoroscopic images with the prostate TRUS images. The technique utilizes a set of fiducials that are detectable by both imaging modalities. The method was tested using a prostate mimicking phantom. The results demonstrate that the method is sufficiently accurate and practical for clinical use.
Ultrasound image segmentation is challenging due to speckles, depth-dependent signal attenuation, low signal-to- noise ratio, and direction-dependent edge contrast. In addition, transrectal ultrasound (TRUS) prostate images are often corrupted by acoustic shadowing caused by calcifications, bowel gas, protein deposit artifacts, etc., making segmentation difficult. In such cases, traditional edge detection algorithms without adequate preprocessing have limited success. The original sticks algorithm reduces speckles while enhancing contrast. It assumes that in a pixel neighborhood, reflectors of different orientations with respect to the incident ultrasound beam are equally likely, which is not the cast in practice. Even though some variations of the original sticks algorithm estimate poor probabilities from the image or from the imaging process, no high-level information about the geometry of the object of interest is utilized. As a result, both non-prostate structures and the true boundaries are equally enhanced. This paper presents an extension to the original sticks algorithm, which incorporates high-level knowledge of prostate shape to selectively enhance the prostate edge contrast while suppressing non-prostate structures. The improved algorithm shows that this extension preserves the prostate boundaries while providing superior noise reduction especially in the interior prostate region, which can lead to more accurate segmentation of the prostate.
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