Trans-Rectal Optical Tomography Reconstruction Using 3-Dimensional Spatial Prior Extracted From Sparse 2-Dimensional Trans-Rectal Ultrasound Imagery

Accurate prostate segmentation in trans-rectal ultrasound (TRUS) imagery is an important step in different clinical applications, and it is particularly necessary for providing a 3-dimensional spatial prior to guide the image reconstruction of trans-rectal optical tomography for prostate cancer detection. Utilizing the US prior to guide near infrared tomography reconstruction could be performed by direct segmentation of the US image. Therefore, 2-dimensional segmentation of the axial TRUS images are performed extensively, however, 2-dimensional segmentation of the sagittal TRUS images are challenging, due to more complexities in contrast, morphological features and image artifacts, as well as significant inter-subject variations of the prostate shape and size. We develop a routine of segmenting 2-dimensional TRUS images obtained from canine prostate, based on the combination of a Snake's algorithm and selected manual segmentation. The segmentations obtained from a sparse set of axial and sagittal images are aligned to form the 3-dimensional contour of a prostate. The resulted prostate profile is implemented as the spatial prior to constrain image reconstruction of trans-rectal optical tomography. The trans-rectal optical tomography images reconstructed with the prostate profile prior are compared with those reconstructed without any spatial prior by monitoring oxygen saturation (StO2) and total hemoglobin concentration ([HbT]) in lesions of a canine prostate.Electrical Engineerin

A coarse-to-fine approach to prostate boundary segmentation in ultrasound images

BACKGROUND: In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented. METHODS: A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation. RESULTS: A total average similarity of 98.76%(± 0.68) with gold standards was achieved. CONCLUSION: The proposed approach represents a robust and accurate approach to prostate segmentation

An Information Tracking Approach to the Segmentation of Prostates in Ultrasound Imaging

Outlining of the prostate boundary in ultrasound images is a very useful procedure performed and subsequently used by clinicians. The contribution of the resulting segmentation is twofold. First of all, the segmentation of the prostate glands can be used to analyze the size, geometry, and volume of the gland. Such analysis is useful as it is known that the former quantities used in conjunction with a PSA blood test can be used as an indicator of malignancy in the gland itself. The second purpose of accurate segmentation is for treatment planning purposes. In brachetherapy, commonly used to treat localized prostate cancer, the accurate location of the prostate must be found so that the radioactive seeds can be placed precisely in the malignant regions. Unfortunately, the current method of segmentation of ultrasound images is performed manually by expert radiologists. Due to the abundance of ultrasound data, the process of manual segmentation can be extremely time consuming and inefficient. A much more desirable way to perform the segmentation process is through automatic procedures, which should be able to accurately and efficiently extract the boundary of the prostate gland with minimal user intervention. This is the ultimate goal of the proposed approach. The proposed segmentation algorithm uses a probability distribution tracking framework to accurately and efficiently perform the task at hand. The basis for this methodology is to extract image and shape features from available manually segmented ultrasound images for which the actual prostate region is known. Then, the segmentation algorithm seeks a region in new ultrasound images whose features closely mirror the learned features of known prostate regions. Promising results were achieved using this method in a series of in silico and in vivo experiments

An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

Topological MRI Prostate Segmentation Method

The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, using an average Dice Similarity Coefficient (DSC). Obtained quantitative results have shown a good approximation of the segmented prostate

Reinforced Segmentation of Images Containing One Object of Interest

In many image-processing applications, one object of interest must be segmented. The techniques used for segmentation vary depending on the particular situation and the specifications of the problem at hand. In methods that rely on a learning process, the lack of a sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. The performance of some other methods may suffer from frequent user interactions to determine the critical segmentation parameters. Also, none of the existing approaches use online (permanent) feedback, from the user, in order to evaluate the generated results. Considering the above factors, a new multi-stage image segmentation system, based on Reinforcement Learning (RL) is introduced as the main contribution of this research. In this system, the RL agent takes specific actions, such as changing the tasks parameters, to modify the quality of the segmented image. The approach starts with a limited number of training samples and improves its performance in the course of time. In this system, the expert knowledge is continuously incorporated to increase the segmentation capabilities of the method. Learning occurs based on interactions with an offline simulation environment, and later online through interactions with the user. The offline mode is performed using a limited number of manually segmented samples, to provide the segmentation agent with basic information about the application domain. After this mode, the agent can choose the appropriate parameter values for different processing tasks, based on its accumulated knowledge. The online mode, consequently, guarantees that the system is continuously training and can increase its accuracy, the more the user works with it. During this mode, the agent captures the user preferences and learns how it must change the segmentation parameters, so that the best result is achieved. By using these two learning modes, the RL agent allows us to optimally recognize the decisive parameters for the entire segmentation process

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Prostate Segmentation and Regions of Interest Detection in Transrectal Ultrasound Images

The early detection of prostate cancer plays a significant role in the success of treatment and outcome. To detect prostate cancer, imaging modalities such as TransRectal UltraSound (TRUS) and Magnetic Resonance Imaging (MRI) are relied on. MRI images are more comprehensible than TRUS images which are corrupted by noise such as speckles and shadowing. However, MRI screening is costly, often unavailable in many community hospitals, time consuming, and requires more patient preparation time. Therefore, TRUS is more popular for screening and biopsy guidance for prostate cancer. For these reasons, TRUS images are chosen in this research. Radiologists first segment the prostate image from ultrasound image and then identify the hypoechoic regions which are more likely to exhibit cancer and should be considered for biopsy. In this thesis, the focus is on prostate segmentation and on Regions of Interest (ROI)segmentation. First, the extraneous tissues surrounding the prostate gland are eliminated. Consequently, the process of detecting the cancerous regions is focused on the prostate gland only. Thus, the diagnosing process is significantly shortened. Also, segmentation techniques such as thresholding, region growing, classification, clustering, Markov random field models, artificial neural networks (ANNs), atlas-guided, and deformable models are investigated. In this dissertation, the deformable model technique is selected because it is capable of segmenting difficult images such as ultrasound images. Deformable models are classified as either parametric or geometric deformable models. For the prostate segmentation, one of the parametric deformable models, Gradient Vector Flow (GVF) deformable contour, is adopted because it is capable of segmenting the prostate gland, even if the initial contour is not close to the prostate boundary. The manual segmentation of ultrasound images not only consumes much time and effort, but also leads to operator-dependent results. Therefore, a fully automatic prostate segmentation algorithm is proposed based on knowledge-based rules. The new algorithm results are evaluated with respect to their manual outlining by using distance-based and area-based metrics. Also, the novel technique is compared with two well-known semi-automatic algorithms to illustrate its superiority. With hypothesis testing, the proposed algorithm is statistically superior to the other two algorithms. The newly developed algorithm is operator-independent and capable of accurately segmenting a prostate gland with any shape and orientation from the ultrasound image. The focus of the second part of the research is to locate the regions which are more prone to cancer. Although the parametric dynamic contour technique can readily segment a single region, it is not conducive for segmenting multiple regions, as required in the regions of interest (ROI) segmentation part. Since the number of regions is not known beforehand, the problem is stated as 3D one by using level set approach to handle the topology changes such as splitting and merging the contours. For the proposed ROI segmentation algorithm, one of the geometric deformable models, active contours without edges, is used. This technique is capable of segmenting the regions with either weak edges, or even, no edges at all. The results of the proposed ROI segmentation algorithm are compared with those of the two experts' manual marking. The results are also compared with the common regions manually marked by both experts and with the total regions marked by either expert. The proposed ROI segmentation algorithm is also evaluated by using region-based and pixel-based strategies. The evaluation results indicate that the proposed algorithm produces similar results to those of the experts' manual markings, but with the added advantages of being fast and reliable. This novel algorithm also detects some regions that have been missed by one expert but confirmed by the other. In conclusion, the two newly devised algorithms can assist experts in segmenting the prostate image and detecting the suspicious abnormal regions that should be considered for biopsy. This leads to the reduction the number of biopsies, early detection of the diseased regions, proper management, and possible reduction of death related to prostate cancer

Deformable models for adaptive radiotherapy planning

Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely deformable models (FDM) and shape constrained deformable models (SCDMs) were used to automatically delineate the GTV for brain cancer and prostate cancer. Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A series of low level image segmentation methodologies are cascaded to form a case-wise fully automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs) were used to evaluate the contours. Results shown a good agreement between clinical contours and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%. The second significant contribution is a novel development to the active shape model (ASM), a profile feature was selected from pre-computed texture features by minimising the Mahalanobis distance (MD) to obtain the most distinct feature for each landmark, instead of conventional image intensity. A new group-wise registration scheme was applied to solve the correspondence definition within the training data. This ASM model was used to delineated prostate GTV on CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81. The last contribution is a fully automatic active appearance model (AAM) which captures image appearance near the GTV boundary. The image appearance of inner GTV was discarded to spare the potential disruption caused by brachytherapy seeds or gold markers. This model outperforms conventional AAM at the prostate base and apex region by involving surround organs. The overall mean DSC for this case is 0.85