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Purpose: To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modality in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker (PM) method, where the ultrasound/photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument., Methods: An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principle of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot., Results: The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics (i.e., 1.15 ± 0.29 mm from the experimental evaluation). The result is also comparable with our previous approach (i.e., 1.05 ± 0.37 mm), and the proposed method can be implemented with a normal white light stereo camera and does not require highly accurate localization of the PM., Conclusion: The proposed frame registration algorithm enabled a simple yet efficient integration of commercial US/PA imaging system into laparoscopic surgical setting by leveraging the characteristic properties of acoustic wave propagation and laser excitation, contributing to automated US/PA image-guided surgical intervention applications., (© 2023. CARS.)

Quantitative tissue stiffness characterization using ultrasound (US) has been shown to improve prostate cancer (PCa) detection in multiple studies. Shear wave absolute vibro-elastography (SWAVE) allows quantitative and volumetric assessment of tissue stiffness using external multifrequency excitation. This article presents a proof of concept of a first-of-a-kind 3-D hand-operated endorectal SWAVE system designed to be used during systematic prostate biopsy. The system is developed with a clinical US machine, requiring only an external exciter that can be mounted directly to the transducer. Subsector acquisition of radio frequency (RF) data allows imaging of shear waves with a high effective frame rate (up to 250 Hz). The system was characterized using eight different quality assurance phantoms. Due to the invasive nature of prostate imaging, at this early stage of development, validation of in vivo human tissue was instead carried out by intercostally scanning the livers of n = 7 healthy volunteers. The results are compared with 3-D magnetic resonance elastography (MRE) and an existing 3-D SWAVE system with a matrix array transducer (M-SWAVE). High correlations were found with MRE (99% in phantoms, 94% in liver data) and with M-SWAVE (99% in phantoms, 98% in liver data).

Purpose: Prostate imaging to guide biopsy remains unsatisfactory, with current solutions suffering from high complexity and poor accuracy and reliability. One novel entrant into this field is micro-ultrasound (microUS), which uses a high-frequency imaging probe to achieve very high spatial resolution, and achieves prostate cancer detection rates equivalent to multiparametric magnetic resonance imaging (mpMRI). However, the ExactVu transrectal microUS probe has a unique geometry that makes it challenging to acquire controlled, repeatable three-dimensional (3D) transrectal ultrasound (TRUS) volumes. We describe the design, fabrication, and validation of a 3D acquisition system that allows for the accurate use of the ExactVu microUS device for volumetric prostate imaging., Methods: The design uses a motorized, computer-controlled brachytherapy stepper to rotate the ExactVu transducer about its axis. We perform geometric validation using a phantom with known dimensions and compare performance with magnetic resonance imaging (MRI) using a commercial quality assurance anthropomorphic prostate phantom., Results: Our geometric validation shows accuracy of 1 mm or less in all three directions, and images of an anthropomorphic phantom qualitatively match those acquired using MRI and show good agreement quantitatively., Conclusion: We describe the first system to acquire robotically controlled 3D microUS images using the ExactVu microUS system. The reconstructed 3D microUS images are accurate, which will allow for future applications of the ExactVu microUS system in prostate specimen and in vivo imaging., (© 2023. CARS.)

Purpose: Pathology from trans-perineal template mapping biopsy (TTMB) can be used as labels to train prostate cancer classifiers. In this work, we propose a framework to register TTMB cores to advanced volumetric ultrasound data such as multi-parametric transrectal ultrasound (mpTRUS)., Methods: The framework has mainly two steps. First, needle trajectories are calculated with respect to the needle guiding template-considering deflections in their paths. In standard TTMB, a sparsely sampled ultrasound volume is taken prior to the procedure which contains the template overlaid on top of it. The position of this template is detected automatically, and the cores are mapped following the calculated needle trajectories. Second, the TTMB volume is aligned to the mpTRUS volume by a two-step registration method. Using the same transformations from the registration step, the cores are registered from the TTMB volume to the mpTRUS volume., Results: TTMB and mpTRUS of 10 patients were available for this work. The target registration errors (TRE) of the volumes using landmarks picked by three research assistants (RA) and one radiation oncologist (RO) were on average 1.32 ± 0.7 mm and 1.03 ± 0.6 mm, respectively. Additionally, on average, our framework takes only 97 s to register the cores., Conclusion: Our proposed framework allows a quick way to find the spatial location of the cores with respect to volumetric ultrasound. Furthermore, knowing the correct location of the pathology will facilitate focal treatment and will aid in training imaging-based cancer classifiers., (© 2022. CARS.)

Purpose: In low-dose-rate prostate brachytherapy (LDR-PB), treatment planning is the process of determining the arrangement of implantable radioactive sources that radiates the prostate while sparing healthy surrounding tissues. Currently, these plans are prepared manually by experts incorporating the centre's planning style and guidelines. In this article, we develop a novel framework that can learn a centre's planning strategy and automatically reproduce rapid clinically acceptable plans., Methods: The proposed framework is based on conditional generative adversarial networks that learn our centre's planning style using a pool of 931 historical LDR-PB planning data. Two additional losses that help constrain prohibited needle patterns and produce similar-looking plans are also proposed. Once trained, this model generates an initial distribution of needles which is passed to a planner. The planner then initializes the sources based on the predicted needles and uses a simulated annealing algorithm to optimize their locations further., Results: Quantitative analysis was carried out on 170 cases which showed the generated plans having similar dosimetry to that of the manual plans but with significantly lower planning durations. Indeed, on the test cases, the clinical target volumes achieving [Formula: see text] of the prescribed dose for the generated plans was on average [Formula: see text] ([Formula: see text] for manual plans) with an average planning time of [Formula: see text] min ([Formula: see text] min for manual plans). Further qualitative analysis was conducted by an expert planner who accepted [Formula: see text] of the plans with some changes ([Formula: see text] requiring minor changes & [Formula: see text] requiring major changes)., Conclusion: The proposed framework demonstrated the ability to rapidly generate quality treatment plans that not only fulfil the dosimetric requirements but also takes into account the centre's planning style. Adoption of such a framework would save significant amount of time and resources spent on every patient; boosting the overall operational efficiency of this treatment.

PCa is a disease with a wide range of tissue patterns and this adds to its classification difficulty. Moreover, the data source heterogeneity, i.e. inconsistent data collected using different machines, under different conditions, by different operators, from patients of different ethnic groups, etc., further hinders the effectiveness of training a generalized PCa classifier. In this paper, for the first time, a Generative Adversarial Network (GAN)-based three-player minimax game framework is used to tackle data source heterogeneity and to improve PCa classification performance, where a proposed modified U-Net is used as the encoder. Our dataset consists of novel high-frequency ExactVu ultrasound (US) data collected from 693 patients at five data centers. Gleason Scores (GSs) are assigned to the 12 prostatic regions of each patient. Two classification tasks: benign vs. malignant and low- vs. high-grade, are conducted and the classification results of different prostatic regions are compared. For benign vs. malignant classification, the three-player minimax game framework achieves an Area Under the Receiver Operating Characteristic (AUC) of 93.4%, a sensitivity of 95.1% and a specificity of 87.7%, respectively, representing significant improvements of 5.0%, 3.9%, and 6.0% compared to those of using heterogeneous data, which confirms its effectiveness in terms of PCa classification.

Purpose: The purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation., Methods and Materials: 13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets. Prostate contours were also deformed, using a finite-element model, to take into account the effect of the TRUS probe pressure. Prostate dosimetry parameters were obtained for fluoroscopic and CBCT-dosimetry approaches and compared with the standard-of-care Day-0 postimplant CT dosimetry., Results: High linear correlation (R 2  > 0.8) was observed in the measured values of prostate D 90% , V 100% , and V 150% , between the two intraoperative dosimetry approaches. The prostate D 90% and V 100% obtained from intraoperative dosimetry methods were in agreement with the postimplant CT dosimetry. Only the prostate V 150% was on average 4.1% (p-value <0.05) higher in the CBCT-dosimetry approach and 6.7% (p-value <0.05) higher in postimplant CT dosimetry compared with the fluoroscopic dosimetry approach. Deformation of the prostate by the ultrasound probe appeared to have a minimal effect on prostate dosimetry., Conclusions: The results of this study have shown that both of the proposed dosimetric evaluation approaches have potential for real-time intraoperative dosimetry., (Copyright © 2020 American Brachytherapy Society. All rights reserved.)

Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine learning-based methods have the potential to improve efficient throughput of large volumes of slides while decreasing variability, but they are not easy to develop because they require substantial amounts of labeled training data. In this paper, we propose a deep learning-based classification technique and data augmentation methods for accurate grading of PCa in histopathology images in the presence of limited data. Our method combines the predictions of three separate convolutional neural networks (CNNs) that work with different patch sizes. This enables our method to take advantage of the greater amount of contextual information in larger patches as well as greater quantity of smaller patches in the labeled training data. The predictions produced by the three CNNs are combined using a logistic regression model, which is trained separately after the CNN training. To effectively train our models, we propose new data augmentation methods and empirically study their effects on the classification accuracy. The proposed method achieves an accuracy of [Formula: see text] in classifying cancerous patches versus benign patches and an accuracy of [Formula: see text] in classifying low-grade (i.e., Gleason grade 3) from high-grade (i.e., Gleason grades 4 and 5) patches. The agreement level of our automatic grading method with expert pathologists is within the range of agreement between pathologists. Our experiments indicate that data augmentation is necessary for achieving expert-level performance with deep learning-based methods. A combination of image-space augmentation and feature-space augmentation leads to the best results. Our study shows that well-designed and properly trained deep learning models can achieve PCa Gleason grading accuracy that is comparable to an expert pathologist.

A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear array of the TRUS probe during the brachytherapy procedure in which needles are used for insertion of stranded seeds. Gold standard for the location of seeds on TRUS data were obtained with the guidance of the complete reconstruction of the seed locations from multiple C-arm fluoroscopy views and used in the creation of the training set. We designed and trained a convolutional neural network (CNN) model that worked on 3D cubical sub-regions of the TRUS images, that will be referred to as patches, representing seed, non-seed within a needle track and non-seed elsewhere in the images. The models were trained with these patches to detect the needle track first and then the individual seeds within the needle track. A leave-one-out cross validation approach was used to test the model on the data from eight of the patients, for whom accurate seed locations were available from fluoroscopic imaging. The total inference time was about 7 min for needle track detection in each patient's image and approximately 1 min for seed detection in each needle, leading to a total seed detection time of less than 15 min. Our seed detection algorithm achieved [Formula: see text] precision, [Formula: see text] recall and [Formula: see text] F1&#95;score. The results from our CNN-based method were compared to manual seed localization performed by an expert. The CNN model yielded higher precision (lower false discovery rate) compared to the manual method. The automated approach requires little modification to the current clinical setups and offers the prospect of application in real time intraoperative dosimetric analysis of the implant.

The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or insufficient landmarks or strong artifacts. We devise a method, based on convolutional neural networks (CNNs), that produces accurate segmentations on easy and difficult images alike. We propose two strategies to achieve improved segmentation accuracy on difficult images. First, for CNN training we adopt an adaptive sampling strategy, whereby the training process is encouraged to pay more attention to images that are difficult to segment. Secondly, we train a CNN ensemble and use the disagreement among this ensemble to identify uncertain segmentations and to estimate a segmentation uncertainty map. We improve uncertain segmentations by utilizing the prior shape information in the form of a statistical shape model. Our method achieves Hausdorff distance of 2.7 ± 2.3 mm and Dice score of 93.9 ± 3.5%. Comparisons with several competing methods show that our method achieves significantly better results and reduces the likelihood of committing large segmentation errors. Furthermore, our experiments show that our approach to estimating segmentation uncertainty is better than or on par with recent methods for estimation of prediction uncertainty in deep learning models. Our study demonstrates that estimation of model uncertainty and use of prior shape information can significantly improve the performance of CNN-based medical image segmentation methods, especially on difficult images., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.

Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies., Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts., Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019., Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic., Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60., Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.

We compare different possible scanning geometries for prostate photoacoustic tomography (PAT) while considering a realistic reconstruction scenario in which the limited view of the prostate and the directivity effect of the transducer are considered. Simulations and experiments confirm that an intra-operative configuration in which the photoacoustic signal is received by a pickup transducer from the anterior surface of the prostate provides the best approach. We propose a PAT acquisition system that includes a da Vinci system controlled by the da Vinci Research Kit, an illumination laser, and an ultrasound machine with parallel data acquisition. The robot maneuvers the pickup transducer to form a cylindrical detection surface around the prostate. The robot is programmed to acquire trajectories in which the transducer face is parallel to and oriented toward a rotational tomography axis, while the laser is fired and PAT data are collected at regular intervals. We present our initial images acquired with this novel system.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Purpose: In the current standard of care, real-time transrectal ultrasound (TRUS) is commonly used for prostate brachytherapy guidance. As TRUS provides limited soft tissue contrast, segmenting the prostate gland in TRUS images is often challenging and subject to inter-observer and intra-observer variability, especially at the base and apex where the gland boundary is hard to define. Magnetic resonance imaging (MRI) has higher soft tissue contrast allowing the prostate to be contoured easily. In this paper, we aim to show that prostate segmentation in TRUS images informed by MRI priors can improve on prostate segmentation that relies only on TRUS images., Methods: First, we compare the TRUS-based prostate segmentation used in the treatment of 598 patients with a high-quality MRI prostate atlas and observe inconsistencies at the apex and base. Second, motivated by this finding, we propose an alternative TRUS segmentation technique that is fully automatic and uses MRI priors. The algorithm uses a convolutional neural network to segment the prostate in TRUS images at mid-gland, where the gland boundary can be clearly seen. It then reconstructs the gland boundary at the apex and base with the aid of a statistical shape model built from an MRI atlas of 78 patients., Results: Compared to the clinical TRUS segmentation, our method achieves similar mid-gland segmentation results in the 598-patient database. For the seven patients who had both TRUS and MRI, our method achieved more accurate segmentation of the base and apex with the MRI segmentation used as ground truth., Conclusion: Our results suggest that utilizing MRI priors in TRUS prostate segmentation could potentially improve the performance at base and apex.

PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue. RESULTS  : Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization. CONCLUSION  : Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

The purpose of this work was to assess trans-perineal prostate magnetic resonance elastography (MRE) for (1) repeatability in phantoms/volunteers and (2) diagnostic power as correlated with histopathology in prostate cancer patients. The three-dimensional (3D) displacement field was obtained using a fractionally encoded gradient echo sequence using a custom-made transducer. The repeatability of the method was assessed based on three repeat studies and by changing the driving frequency by 3% in studies on a phantom and six healthy volunteers. Subsequently, 11 patients were examined with MRE prior to radical prostatectomy. The areas under the receiver operating characteristic curves were calculated using a windowed voxel-to-voxel approach by comparing the 2D registered slides, masked with the Gleason score. For the repeatability study, the average intraclass correlation coefficient for elasticity images was 99% for repeat phantom studies, 98% for ±6 Hz phantom studies, 95% for volunteer repeat studies with 2 min acquisition time, 82% for ±2 Hz volunteer studies with 2 min acquisition time and 73% for repeat volunteer studies with 8 min acquisition time. For the patient study, the average elasticity was 8.2 ± 1.7 kPa in the prostate capsule, 7.5 ± 1.9 kPa in the peripheral zone (PZ), 9.7 ± 3.0 kPa in the central gland (CG) and 9.0 ± 3.4 kPa in the transition zone. In the patient study, cancerous tissue with Gleason score at least 3 + 3 was significantly (p < 0.05) different from normal tissue in 10 out of 11 cases with tumors in the PZ, and 6 out of 9 cases with tumors in the CG. However, the overall case-averaged area under the curve was 0.72 in the PZ and 0.67 in the CG. Cancerous tissue was not always stiffer than normal tissue. The inversion algorithm was sensitive to (i) vibration amplitude and displacement nodes and (ii) misalignment of the 3D wave field due to subject movement., (Copyright © 2014 John Wiley & Sons, Ltd.)

Purpose: To provide unencumbered real-time ultrasound image guidance during robot-assisted laparoscopic radical prostatectomy, we developed a robotic transrectal ultrasound system that tracks the da Vinci® Surgical System instruments. We describe our initial clinical experience with this system., Materials and Methods: After an evaluation in a canine model, 20 patients were enrolled in the study. During each procedure the transrectal ultrasound transducer was manually positioned using a brachytherapy stabilizer to provide good imaging of the prostate. Then the transrectal ultrasound was registered to the da Vinci robot by a previously validated procedure. Finally, automatic rotation of the transrectal ultrasound was enabled such that the transrectal ultrasound imaging plane safely tracked the tip of the da Vinci instrument controlled by the surgeon, while real-time transrectal ultrasound images were relayed to the surgeon at the da Vinci console. Tracking was activated during all critical stages of the surgery., Results: The transrectal ultrasound robot was easy to set up and use, adding 7 minutes (range 5 to 14) to the procedure. It did not require an assistant or additional control devices. Qualitative feedback was acquired from the surgeons, who found transrectal ultrasound useful in identifying the urethra while passing the dorsal venous complex suture, defining the prostate-bladder interface during bladder neck dissection, identifying the seminal vesicles and their location with respect to the rectal wall, and identifying the distal prostate boundary at the apex., Conclusions: Real-time, registered robotic transrectal ultrasound guidance with automatic instrument tracking during robot-assisted laparoscopic radical prostatectomy is feasible and potentially useful. The results justify further studies to establish whether the approach can improve procedure outcomes., (Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

The purpose of this work was (1) to develop a magnetic resonance elastography (MRE) system for imaging of the ex vivo human prostate and (2) to assess the diagnostic power of mono-frequency and multi-frequency MRE and diffusion weighted imaging (DWI) alone and combined as correlated with histopathology in a patient study. An electromagnetic driver was designed specifically for MRE studies in small-bore MR scanners. Ex vivo prostate specimens (post-fixation) of 14 patients who underwent radical prostatectomy were imaged with MRE at 7 T (nine cases had DWI). In six patients, the MRE examination was performed at three frequencies (600, 800, 1000 Hz) to extract the power-law exponent Gamma. The images were registered to wholemount pathology slides marked with the Gleason score. The areas under the receiver-operator-characteristic curves (AUC) were calculated. The methods were validated in a phantom study and it was demonstrated that (i) the driver does not interfere with the acquisition process and (ii) the driver can generate amplitudes greater than 100 µm for frequencies less than 1 kHz. In the quantitative study, cancerous tissue with Gleason score at least 3 + 3 was distinguished from normal tissue in the peripheral zone (PZ) with an average AUC of 0.75 (Gd ), 0.75 (Gl ), 0.70 (Gamma-Gd ), 0.68 (apparent diffusion coefficient, ADC), and 0.82 (Gd  + Gl  + ADC). The differentiation between PZ and central gland was modest for Gd (p < 0.07), Gl (p < 0.06) but not significant for Gamma (p < 0.2). A correlation of 0.4 kPa/h was found between the fixation time of the prostate specimen and the stiffness of the tissue, which could affect the diagnostic power results. DWI and MRE may provide complementary information; in fact MRE performed better than ADC in distinguishing normal from cancerous tissue in some cases. Multi-frequency (Gamma) analysis did not appear to improve the results. However, in light of the effect of tissue fixation, the clinical implication of our results may be inconclusive and more experiments are needed., (Copyright © 2014 John Wiley & Sons, Ltd.)

Purpose: Dynamic dosimetry is becoming the standard to evaluate the quality of radioactive implants during brachytherapy. For this, it is essential to obtain a 3D visualization of the implanted seeds and their relative position to the prostate. A method was developed to obtain a robust and precise segmentation of seeds in C-arm images, and this approach was tested using clinical datasets., Method: A region-based implicit active contour approach was used to delineate implanted seeds. Then, a template-based matching was employed to segment iodine implants whereas a K-means algorithm is implemented to resolve palladium seed clusters. To validate the method, 55 C-arm images from 10 patients were used for the segmentation of iodine sources, whereas 225 C-arm images from 16 patients were used for the palladium case., Results: Compared to manual ground truth segmentation of 6,002 iodine seeds and 15,354 palladium seeds, 98.7 % of iodine sources were automatically detected and declustered showing a false-positive rate of only 1.7 %. A total of 98.7 % of palladium sources were automatically detected and declustered with a false-positive rate of only 2.0 %., Conclusion: An automated segmentation method was developed that is able to perform the identification and annotation processes of seeds on par with a human expert. This method was shown to be robust and suitable for integration in the dynamic dosimetry workflow of prostate brachytherapy interventions.

Purpose: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images., Methods: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM., Results: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leave-one-patient-out validation., Conclusions: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.

In this paper, a novel computer-based virtual training system for prostate brachytherapy is presented. This system incorporates, in a novel way, prior methodologies of ultrasound image synthesis and haptic transrectal ultrasound (TRUS) transducer interaction in a complete simulator that allows a trainee to maneuver the needle and the TRUS, to see the resulting patient-specific images and feel the interaction forces. The simulated TRUS images reflect the volumetric tissue deformation and comprise validated appearance models for the needle and implanted seeds. Rendered haptic forces use validated models for needle shaft flexure and friction, tip cutting, and deflection due to bevel. This paper also presents additional new features that make the simulator complete, in the sense that all aspects of the brachytherapy procedure as practiced at many cancer centers are simulated, including simulations of seed unloading, fluoroscopy imaging, and transversal/sagittal TRUS plane switching. For real-time rendering, methods for fast TRUS-needle-seed image formation are presented. In addition, the simulator computes real-time dosimetry, allowing a trainee to immediately see the consequence of planning changes. The simulation is also patient specific, as it allows the user to import the treatment plan for a patient together with the imaging data in order for a physician to practice an upcoming procedure or for a medical resident to train using typical implant scenarios or rarely encountered cases.

Purpose: To demonstrate that manual prostate segmentation in transrectal ultrasound images can be replaced with semiautomatic segmentation., Methods and Materials: Semiautomatic segmentation using a tapered ellipsoid model was applied to transrectal ultrasound images. Region-based volumetric evaluation was performed between original and physician-reviewed semiautomatic contours. For dosimetric assessment, treatment plans generated on semiautomatic contours were overlaid on physician-reviewed semiautomatic contours and dose parameters were computed. To establish a threshold for the acceptable amount of dosimetric degradation below which the adoption of semiautomatic planning is unacceptable, the range of variability in dosimetric quality attributed to manual variability was obtained and compared with that of semiautomatic contours., Results: An average volume error (1-Dice similarity coefficient) of less than 7% between semiautomatic and manual volumes (140 cases) was obtained. The difference between the mean V(100) of plans created for semiautomatic contours then overlaid on physician-reviewed semiautomatic contours and the original V(100) values, that is, before overlaying on the physician-reviewed contours (41 cases) was lower than 5%. An average total duration of 2-4min, which includes algorithm initialization, 11.67±3.57s algorithm time, and contour modification is required per case. This algorithm is being used at the British Columbia Cancer Agency and to this date has been applied for the treatment of more than 600 patients., Conclusions: In terms of volumetric and dosimetric accuracy, the proposed algorithm is a suitable replacement for manual segmentation in the context of our planning technique. The benefits are shorter segmentation times; greater consistency; less reliance on user experience; and smooth, symmetric contours., (Copyright © 2013 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.)

Delineation of the prostate from transrectal ultrasound images is a necessary step in several computer-assisted clinical interventions, such as low dose rate brachytherapy. Current approaches to user segmentation require user intervention and therefore it is subject to user errors. It is desirable to have a fully automatic segmentation for improved segmentation consistency and speed. In this paper, we propose a multi-atlas fusion framework to automatically segment prostate transrectal ultrasound images. The framework initially registers a dataset of a priori segmented ultrasound images to a target image. Subsequently, it uses the pairwise similarity of registered prostate shapes, which is independent of the image-similarity metric optimized during the registration process, to prune the dataset prior to the fusion and consensus segmentation step. A leave-one-out cross-validation of the proposed framework on a dataset of 50 transrectal ultrasound volumes obtained from patients undergoing brachytherapy treatment shows that the proposed is clinically robust, accurate and reproducible.

This paper presents an analysis of the high resolution histopathology images of the prostate with a focus on the evolution of morphological gland features in prostatic adenocarcinoma. Here we propose a novel technique of labeling individual glands as malignant or benign. In the first step, the gland and nuclei objects of the images are automatically segmented. Individual gland units are segmented out by consolidating their lumina with the surrounding layers of epithelium and nuclei. The nuclei objects are segmented by using a marker controlled watershed algorithm. Two new features, Number of Nuclei Layer (N(NL)) and Ratio of Epithelial layer area to Lumen area (R(EL)) have been extracted from the segmented units. The main advantage of this approach is that it can detect individual malignant gland units, irrespective of neighboring histology and/or the spatial extent of the cancer. The proposed algorithm has been tested on 40 histopathology scenes taken from 10 high resolution whole mount images and achieved a sensitivity of 0.83 and specificity of 0.81 in a leave-75%-out cross-validation.

Purpose: In prostate brachytherapy, intraoperative dosimetry would allow for evaluation of the implant quality while the patient is still in treatment position. Such a mechanism, however, requires 3-D visualization of the deposited seeds relative to the prostate. It follows that accurate and robust seed segmentation is of critical importance in achieving intraoperative dosimetry., Methods: Implanted iodine brachytherapy seeds are segmented via a region-based implicit active contour model. Overlapping seed groups are then resolved using a template-based declustering technique., Results: Ground truth seed coordinates were obtained through manual segmentation. A total of 57 clinical C-arm images from 10 patients were used to validate the proposed algorithm. This resulted in two failed images and a 96.0% automatic detection rate with a corresponding 2.2% false-positive rate in the remaining 55 images. The mean centroid error between the manual and automatic segmentations was 1.2 pixels., Conclusions: Robust and accurate iodine seed segmentation can be achieved through the proposed segmentation workflow.

Purpose: To develop an image-based technique capable of detection and grading of prostate cancer, which combines features extracted from multiparametric MRI into a single parameter map of cancer probability., Materials and Methods: A combination of features extracted from diffusion tensor MRI and dynamic contrast enhanced MRI was used to characterize biopsy samples from 29 patients. Support vector machines were used to separate the cancerous samples from normal biopsy samples and to compute a measure of cancer probability, presented in the form of a cancer colormap. The classification results were compared with the biopsy results and the classifier was tuned to provide the largest area under the receiver operating characteristic (ROC) curve. Based solely on the tuning of the classifier on the biopsy data, cancer colormaps were also created for whole-mount histopathology slices from four radical prostatectomy patients., Results: An area under ROC curve of 0.96 was obtained on the biopsy dataset and was validated by a "leave-one-patient-out" procedure. The proposed measure of cancer probability shows a positive correlation with Gleason score. The cancer colormaps created for the histopathology patients do display the dominant tumors. The colormap accuracy increases with measured tumor area and Gleason score., Conclusion: Dynamic contrast enhanced imaging and diffusion tensor imaging, when used within the framework of supervised classification, can play a role in characterizing prostate cancer., (Copyright © 2012 Wiley Periodicals, Inc.)

We aim to compute the movement of permanent stranded implant brachytherapy radioactive sources (seeds) in the prostate from the planned seed distribution to the intraoperative fluoroscopic distribution, and then to the postimplant computed tomography (CT) distribution. We present a novel approach to matching the seeds in these distributions to the plan by grouping the seeds into needle tracks. First, we identify the implantation axis using a sample consensus algorithm. Then, we use a network flow algorithm to group seeds into their needle tracks. Finally, we match the needles from the three stages using both their transverse plane location and the number of seeds per needle. We validated our approach on eight clinical prostate brachytherapy cases, having a total of 871 brachytherapy seeds distributed in 193 needles. For the intraoperative and postimplant data, 99.31% and 99.41% of the seeds were correctly assigned, respectively. For both the preplan to fluoroscopic and fluoroscopic to CT registrations, 100% of the needles were correctly matched. We show that there is an average intraoperative seed displacement of 4.94±2.42 mm and a further 2.97±1.81 mm of postimplant movement. This information reveals several directional trends and can be used for quality control, treatment planning, and intraoperative dosimetry that fuses ultrasound and fluoroscopy.

C-arm fluoroscopy images are frequently used for qualitative assessment of prostate brachytherapy. Three-dimensional seed reconstruction from C-arm images is necessary for intraoperative dosimetry and quantitative assessment. Seed reconstruction requires accurately known C-arm poses. We propose to measure the C-arm rotation angles and computationally compensate for inevitable C-arm motion to compute the pose. We compensate the translational motions of a C-arm, such as oscillation, sagging and wheel motion using a three-level optimization algorithm and obviate the need for full pose tracking using external trackers or fiducials. We validated our approach on simulated and 100 clinical data sets from 10 patients and gained on average, a seed matching rate of 98.5%, projection error of 0.33 mm (STD=0.21 mm) and computation time of 19.8s per patient, which must be considered as clinically excellent results. We also show that without motion compensation the reconstruction is likely to fail., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Purpose: Accurate localization of prostate implants from several C-arm images is necessary for ultrasound-fluoroscopy fusion and intraoperative dosimetry. The authors propose a computational motion compensation method for tomosynthesis-based reconstruction that enables 3D localization of prostate implants from C-arm images despite C-arm oscillation and sagging., Methods: Five C-arm images are captured by rotating the C-arm around its primary axis, while measuring its rotation angle using a protractor or the C-arm joint encoder. The C-arm images are processed to obtain binary seed-only images from which a volume of interest is reconstructed. The motion compensation algorithm, iteratively, compensates for 2D translational motion of the C-arm by maximizing the number of voxels that project on a seed projection in all of the images. This obviates the need for C-arm full pose tracking traditionally implemented using radio-opaque fiducials or external trackers. The proposed reconstruction method is tested in simulations, in a phantom study and on ten patient data sets., Results: In a phantom implanted with 136 dummy seeds, the seed detection rate was 100% with a localization error of 0.86 ± 0.44 mm (Mean ± STD) compared to CT. For patient data sets, a detection rate of 99.5% was achieved in approximately 1 min per patient. The reconstruction results for patient data sets were compared against an available matching-based reconstruction method and showed relative localization difference of 0.5 ± 0.4 mm., Conclusions: The motion compensation method can successfully compensate for large C-arm motion without using radio-opaque fiducial or external trackers. Considering the efficacy of the algorithm, its successful reconstruction rate and low computational burden, the algorithm is feasible for clinical use.

In this paper, vibro-elastography (VE), an ultrasound-based method that creates images of tissue viscoelasticity contrast, is evaluated as an imaging modality to visualize and segment the prostate. We report a clinical study to characterize the visibility of the prostate in VE images and the ability to detect the boundary of the gland. Measures for contrast, edge strength characterized by gradient and statistical intensity change at the edge, and the continuity of the edges are proposed and computed for VE and B-mode ultrasound images. Furthermore, using MRI as the gold standard, we compare the error in the computation of the volume of the gland from VE and B-mode images. The results demonstrate that VE images are superior to B-mode images in terms of contrast, with an approximately six fold improvement in contrast-to-noise ratio, and in terms of edge strength, with an approximately two fold improvement in the gradient in the direction normal to the edge. The computed volumes show that the VE images provide an accurate 3D visualization of the prostate with volume errors that are slightly lower than errors computed based on B-mode images. The total gland volume error is 8.8±2.5% for VE vs. MRI and 10.3±4.6% for B-mode vs. MRI, and the total gland volume difference is -4.6±11.1% for VE vs. MRI and -4.1±17.1% for B-mode vs. MRI, averaged over nine patients and three observers. Our results show that viscoelastic mapping of the prostate region using VE images can play an important role in improving the anatomic visualization of the prostate and has the potential of becoming an integral component of interventional procedures such as brachytherapy., (Copyright © 2011 Elsevier B.V. All rights reserved.)

In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a-priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between non-overlapping volumes created from manual and semi-automatic contours from 21 patients was 6.63 ± 0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82 ± 4.15%. The amount of bias in the physicians' delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25 ± 0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14 ± 1s with an additional 32 ± 14s for initialization. By assuming 1-3 min for modification of the contours, if necessary, a total segmentation time of less than 4 min is required, with no additional time required prior to treatment planning. This compares favorably to the 5-15 min manual segmentation time required for experienced individuals. The method is currently used at the British Columbia Cancer Agency (BCCA) Vancouver Cancer Centre as part of the standard treatment routine in low dose rate prostate brachytherapy and is found to be a fast, consistent and accurate tool for the delineation of the prostate gland in ultrasound images., (Copyright © 2010 Elsevier B.V. All rights reserved.)

We aim to compute radioactive stranded-implant displacement during and after prostate brachytherapy. We present the methods used to identify corresponding seeds in planned, intra-operative and postimplant patient data that enable us to compute seed displacements. A minimum cost network flow algorithm is used, on 8 patients, for needle track detection to group seeds into needles that can be matched between datasets. An iterative best line detection algorithm is used both to help with needle detection and to register the different datasets. Our results show that there was an average seed misplacement of 5.08 +/- 2.35 mm during the procedure, which then moved another 3.10 +/- 1.91 mm by the time the quality assurance CT was taken. Several directional trends in different regions of the prostate were noted and commented on.

Intraoperative dosimetry during prostate brachytherapy is a long standing clinical problem. We propose a novel framework to address this problem by reliable detection of a subset of seeds from 3D transrectal ultrasound and registration to fluoroscopy. Seed detection in ultrasound is achieved through template matching in the RF ultrasound domain followed by thresholding and spatial filtering based on the fixed distance between stranded seeds. This subset of seeds is registered to the complete reconstruction of the implant in C-arm fluoroscopy. We report results, validated with a leave-one-needle-out approach, both in a phantom (average post-registration seed distance of 2.5 mm) and in three clinical patient datasets (average error: 3.9 mm over 113 seeds).

During prostate brachytherapy, C-arm flouroscopy images are used for a qualitative assessment of the procedure. Three dimensional reconstruction of the implanted seeds can be used for intraoperative dosimetry and quantitative assessment. Accurate C-arm pose estimation is necessary for 3D localization of the seeds. We propose to measure the C-arm rotation angles and computationally compensate the inevitable C-arm translational motion to estimate the pose. We compensate the oscillation, sagging and wheel motion of the C-arm using a three-level optimization algorithm, without which the reconstruction can fail. We validated our approach on simulated and 10 data sets from 5 patients and gained on average 99.1% success rate, 0.33 mm projection error and computation time of less than one minute per patient, which are clinically excellent results.

Prostate segmentation from trans-rectal transverse B-mode ultrasound images is required for radiation treatment of prostate cancer. Manual segmentation is a time-consuming task, the results of which are dependent on image quality and physicians' experience. This paper introduces a semi-automatic 3D method based on super-ellipsoidal shapes. It produces a 3D segmentation in less than 15 seconds using a warped, tapered ellipsoid fit to the prostate. A study of patient images shows good performance and repeatability. This method is currently in clinical use at the Vancouver Cancer Center where it has become the standard segmentation procedure for low dose-rate brachytherapy treatment.

Needle insertion simulators find use in a number of medical interventions, such as prostate brachytherapy. A needle insertion simulator has three main components: the needle model, the tissue model, and the model of interaction between the needle and the tissue. In this paper, a new methodology is introduced for the joint modeling of tissue and needle-tissue interactions. The approach consists of the measurement of tissue motion using ultrasound, and of the needle position and base force. Tissue motion is determined using a correlation-based algorithm that processes the ultrasound radiofrequency data. The tissue elastic parameters and the parameters of the tissue-needle interaction model are determined by using numerical optimization to match the response of the needle insertion model to the measured data. Phantom experiments were carried out in which a brachytherapy needle was inserted into a two-layer non-homogeneous phantom mimicking a prostate and its surrounding tissue. Experimental results show good agreement with the model obtained. In particular, the parameters of a three-parameter force model were identified for each layer of the phantom to fit the measured force to the simulated one. Also, the Young's modulus of each layer was identified to match the measured and simulated nodal axial displacements. This is the first report of the use of ultrasound radiofrequency data to characterize tissue motion during needle insertion. As the method is non-invasive and does not involve ionizing radiation, its application in patient studies is feasible.

Needle insertion simulators are in demand to train physicians for surgical procedures such as prostate brachytherapy. In order to design a needle insertion simulator, a needle-tissue interaction model is necessary. Such a model can be identified using force and displacement data measured during the insertion of the needle. In this paper, an experiment is conducted in which a needle is inserted into a two-layered PVC phantom, while the needle position and insertion forces are measured and the tissue is imaged using a trans-rectal probe in the sagittal plane. A 2D block matching algorithm is used to estimate tissue deformation from the envelope of the recorded radio-frequency signals. This algorithm can be used to estimate the rotation and displacement of the prostate during prostate brachytherapy. The block matching method was validated in an independent experiment. With the measured force, needle position and tissue displacements, a finite element simulation was conducted to identify the parameters of a needle-tissue force distribution model and Young's moduli of each part of the tissue phantom.

This paper presents a 3D semi-automatic prostate segmentation method for B-mode trans-rectal ultrasound (TRUS) images. Segmentation is based on prior knowledge of the prostate transversal section shape which is assumed to be a tapered ellipse. The approach consists of an initial untapering and warping of the image to make the shape of the prostate approximately elliptical. The prostate contour is found in the untapered, warped images by edge detection and 2D/3D ellipsoidal curve fitting, performed by solving convex optimization problem. This leads to an average total segmentation duration of 7.42s which is more than 40 times faster than manual segmentation. Clinical studies carried out on 58 prostate volume studies show a volume sensitivity and accuracy of approximately 94% and 73% respectively.

This paper presents a needle-tissue interaction model that is a 3D extension of prior work based on needle and tissue models discretized using the Finite Element Method. The use of flexible needles necessitates remeshing the tissue during insertion, since simple mesh-node snapping to the tip can be detrimental to the simulation. In this paper, node repositioning and node addition are the two methods of mesh modification examined for coarse meshes. Our focus is on numerical approaches for fast implementation of these techniques. Although the two approaches compared, namely the Woodbury formula (matrix inversion lemma) and the boundary condition switches, have the same computational complexity, the Woodbury formula is shown to perform faster due to its cache-efficient order of operations. Furthermore, node addition is applied in constant time for both approaches, whereas node repositioning requires longer and variable computational times. A method for rendering the needle forces during simulated insertions into a 3D prostate model has been implemented. Combined with a detailed anatomical segmentation, this will be useful in teaching the practice of prostate brachytherapy. Issues related to discretization of such coupled (e.g., needle-tissue) models are also discussed.

This paper presents a new algorithm for the semi-automatic segmentation of the prostate from B-mode trans-rectal ultrasound (TRUS) images. The segmentation algorithm first uses image warping to make the prostate shape elliptical. Measurement points along the prostate boundary, obtained from an edge-detector, are then used to find the best elliptical fit to the warped prostate. The final segmentation result is obtained by applying a reverse warping algorithm to the elliptical fit. This algorithm was validated using manual segmentation by an expert observer on 17 midgland, pre-operative, TRUS images. Distance-based metrics between the manual and semi-automatic contours showed a mean absolute difference of 0.67 +/- 0.18 mm, which is significantly lower than inter-observer variability. Area-based metrics showed an average sensitivity greater than 97% and average accuracy greater than 93%. The proposed algorithm was almost two times faster than manual segmentation and has potential for real-time applications.

This paper presents a needle-tissue interaction model that is a 3D extension of a prior work based on the finite element method. The model is also adapted to accommodate arbitrary meshes so that the anatomy can effectively be meshed using third-party algorithms. Using this model a prostate brachytherapy simulator is designed to help medical residents acquire needle steering skills. This simulation uses a prostate mesh generated from clinical data segmented as contours on parallel slices. Node repositioning and addition, which are methods for achieving needle-tissue coupling, are discussed. In order to achieve realtime haptic rates, computational approaches to these methods are compared. Specifically, the benefit of using the Woodbury formula (matrix inversion lemma) is studied. Our simulation of needle insertion into a prostate is shown to run faster than 1 kHz.