Transrectal Ultrasound Prostate Images Research Articles

A mathematical and neural network-based hybrid technique for detecting the prostate contour from medical image data

Extraction of prostate contour on transrectal ultrasound (TRUS) is significant in prostate cancer diagnosis. With the low-contrast images of TRUS and the existence of imaging artifacts, detecting the prostate accurately from TRUS images becomes more and more difficult. In this paper, we present four strategies to achieve improved segmentation precision on difficult images. First, to achieve the data sequence, we adopt a variant of the principal curve-based algorithm, where a small part of prior point information is utilized as the approximate initialization. Secondly, we devise an evolution neural network to assist in finding the optimal neural network. Thirdly, we train the fractional-order-based neural network using data sequence as input, thereby decreasing the model error and increasing the precision of results during training. Finally, a smooth and explainable mathematical function of organ boundary is represented by the parameters of a fractional-order-based neural network. The performance of our method was evaluated on two clinical datasets with 1,338 TRUS prostate images using Accuracy (ACC), Dice similarity coefficient (DSC), and Jaccard similarity coefficient (OMG). Results show that our model 1) achieves better performance than the state-of-the-art segmentation models; the corresponding ACC, DSC, and OMG of our method are 95.3 ± 2.2 %, 95.9 ± 2.3 %, and 94.9 ± 2.4 %, respectively, and 2) detects unseen or vague regions satisfactorily.

Prostate Ultrasound Image Segmentation Based on DSU-Net

In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods.

Magnetic resonance imaging and transrectal ultrasound prostate image segmentation based on improved level set for robotic prostate biopsy navigation.

Transrectal ultrasound (TRUS) guided prostate biopsy is a typical early prostate examination. However, the ultrasound imaging suffers from blurred contour, intensity inhomogeneity and small surrounding soft tissue differentiation. To take advantage of clear magnetic resonance imaging (MRI) into robotic prostate biopsy navigation, the prostate regions in the MRI and TRUS images need to be segmented separately. This paper proposes an improved level set segmentation model based on prior shape, which aims to provide a better solution to the prostate segmentation problems in TRUS and MRI. In our segmentation model, the Gaussian probability model is used to establish the statistical learning of the prior shape, and the cosine function is used to represent the energy term fitting of the traditional prior shape and the local intensity information. The experiment results show that the model can adapt to different forms of prostate in MRI and TRUS more accurately, and the prostate biopsy accuracy in our biopsy system can reach 2.24 ± 1.44 mm. This segmentation model has high accuracy, meets the clinical needs in robotic prostate biopsy navigation.

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively typically requires the physician to manually segment the prostate to define the margins used for accurate registration, targeting, and other guidance techniques. However, manual prostate segmentation is a time-consuming and difficult intraoperative process, often occurring while the patient is under sedation (biopsy) or anesthetic (brachytherapy). Minimizing procedure time with a 3D TRUS prostate segmentation method could provide physicians with a quick and accurate prostate segmentation, and allow for an efficient workflow with improved patient throughput to enable faster patient access to care. The purpose of this study was to develop a supervised deep learning-based method to segment the prostate in 3D TRUS images from different facilities, generated using multiple acquisition methods and commercial ultrasound machine models to create a generalizable algorithm for needle-based prostate cancer procedures. Our proposed method for 3D segmentation involved prediction on two-dimensional (2D) slices sampled radially around the approximate central axis of the prostate, followed by reconstruction into a 3D surface. A 2D U-Net was modified, trained, and validated using images from 84 end-fire and 122 side-fire 3D TRUS images acquired during clinical biopsies and brachytherapy procedures. Modifications to the expansion section of the standard U-Net included the addition of 50% dropouts and the use of transpose convolutions instead of standard upsampling followed by convolution to reduce overfitting and improve performance, respectively. Manual contours provided the annotations needed for the training, validation, and testing datasets, with the testing dataset consisting of 20 end-fire and 20 side-fire unseen 3D TRUS images. Since predicting with 2D images has the potential to lose spatial and structural information, comparisons to 3D reconstruction and optimized 3D networks including 3D V-Net, Dense V-Net, and High-resolution 3D-Net were performed following an investigation into different loss functions. An extended selection of absolute and signed error metrics were computed, including pixel map comparisons [dice similarity coefficient (DSC), recall, and precision], volume percent differences (VPD), mean surface distance (MSD), and Hausdorff distance (HD), to assess 3D segmentation accuracy. Overall, our proposed reconstructed modified U-Net performed with a median [first quartile, third quartile] absolute DSC, recall, precision, VPD, MSD, and HD of 94.1 [92.6, 94.9]%, 96.0 [93.1, 98.5]%, 93.2 [88.8, 95.4]%, 5.78 [2.49, 11.50]%, 0.89 [0.73, 1.09]mm, and 2.89 [2.37, 4.35]mm, respectively. When compared to the best-performing optimized 3D network (i.e., 3D V-Net with a Dice plus cross-entropy loss function), our proposed method performed with a significant improvement across nearly all metrics. A computation time <0.7s per prostate was observed, which is a sufficiently short segmentation time for intraoperative implementation. Our proposed algorithm was able to provide a fast and accurate 3D segmentation across variable 3D TRUS prostate images, enabling a generalizable intraoperative solution for needle-based prostate cancer procedures. This method has the potential to decrease procedure times, supporting the increasing interest in needle-based 3D TRUS approaches.

PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

Introduction & Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.

A modified level set algorithm based on point distance shape constraint for lesion and organ segmentation

PurposeThe segmentation of organs and lesions from medical images is a challenging task due to the presents of noise, intensity inhomogeneity, blurry/weak boundaries. In this paper, a point distance shape constraint is proposed and incorporated in the level set framework for the segmentation of objects with various shapes. MethodsThe proposed shape constraint is a linear combination of the Euclidean distance of user selected points. By selecting different numbers of points, it can generate different shape constraints and therefore is more flexible in dealing with different shapes. Then this shape constraint is incorporated into the variational level set framework. A convex relaxation is applied to get a convex model which can be efficiently solved by a primal-dual hybrid gradient algorithm. ResultsThe proposed algorithm is tested on 60 CT images with the nodular type of hepatic cellular cancer (HCC), 100 ultrasound kidney images, 20 prostate MR images, 20 lumbar CT images and 100 transrectal ultrasound prostate images. The algorithms performance is evaluated using a number of metrics by comparison with expert delineations. The validation results show that, for five datasets mentioned previously, the average DSCs of the proposed algorithm are 95.6% ± 1.4%, 94.3% ± 3.1%, 91.3% ± 3.8%, 92.7% ± 1.5% and 94.4% ± 2.2% respectively. Both quantitative and qualitative evaluation confirm that the proposed method can provide more accurate segmentation than four state-of-the-art methods. ConclusionThe proposed point distance shape constraint segmentation model can accurately segment organs and lesions with a number of shapes in medical images.

Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation

Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions.

Multimodal image-guided prostate fusion biopsy based on automatic deformable registration.

Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, [Formula: see text]-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance. The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland. The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar-gelatin phantoms and clinical data of 13 patients confirm the validity of this approach. The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy.

The technological advances in real-time ultrasound image guidance for high-dose-rate (HDR) prostate brachytherapy have placed this treatment modality at the forefront of innovation in cancer radiotherapy. Prostate HDR treatment often involves placing the HDR catheters (needles) into the prostate gland under the transrectal ultrasound (TRUS) guidance, then generating a radiation treatment plan based on CT prostate images, and subsequently delivering high dose of radiation through these catheters. The main challenge for this HDR procedure is to accurately segment the prostate volume in the CT images for the radiation treatment planning. In this study, the authors propose a novel approach that integrates the prostate volume from 3D TRUS images into the treatment planning CT images to provide an accurate prostate delineation for prostate HDR treatment. The authors' approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1-3 min. These TRUS images are used to create prostate contours. The HDR catheters are reconstructed from the intraoperative TRUS and postoperative CT images, and subsequently used as landmarks for the TRUS-CT image fusion. After TRUS-CT fusion, the TRUS-based prostate volume is deformed to the CT images for treatment planning. This method was first validated with a prostate-phantom study. In addition, a pilot study of ten patients undergoing HDR prostate brachytherapy was conducted to test its clinical feasibility. The accuracy of their approach was assessed through the locations of three implanted fiducial (gold) markers, as well as T2-weighted MR prostate images of patients. For the phantom study, the target registration error (TRE) of gold-markers was 0.41 ± 0.11 mm. For the ten patients, the TRE of gold markers was 1.18 ± 0.26 mm; the prostate volume difference between the authors' approach and the MRI-based volume was 7.28% ± 0.86%, and the prostate volume Dice overlap coefficient was 91.89% ± 1.19%. The authors have developed a novel approach to improve prostate contour utilizing intraoperative TRUS-based prostate volume in the CT-based prostate HDR treatment planning, demonstrated its clinical feasibility, and validated its accuracy with MRIs. The proposed segmentation method would improve prostate delineations, enable accurate dose planning and treatment delivery, and potentially enhance the treatment outcome of prostate HDR brachytherapy.

초음파 전립선 영상에서 전립선 경계 분할을 위한 평균 형상 모델

전립선암은 전립선에 나타나는 악성 종양이다. 현재 그 발병률이 높아지고 있다. 전립선암의 구조를 가장 정확하게 확인할 수 있는 검사 방법은 MRI를 이용하는 것이나, 그 비용 때문에 모든 환자에게 적용하기는 어려운 실정이다. 그래서 많은 환자들은 가격이 저렴한 초음파검사를 이용하여 전립선암을 진단하고 있다. 전통적으로 의사들은 영상을 눈으로 확인하여 전립선의 경계를 수동으로 분할하였다. 그러나 수동으로 분할하는 과정은 시간이 많이 소요되며, 의사에 따라서 그 경계가 일정하지 않게 얻어진다. 이 문제를 해결하기 위하여 전립선의 자동 분할에 관한 연구가 되었고, 환자들에게 신뢰를 줄 수 있었다. 본 연구는 초음파 전립선 영상에서 전립선의 경계를 분할하는데 평균 형상 모델을 적용하는 것이다. 먼저, 에지 분포를 이용하여 프로브를 찾고, 프로브와 연결된 두 직선을 찾는다. 이 후에 이 정보를 이용하여 전립선 영상 위에 평균 형상을 위치시킨다. Prostate cancer is a malignant tumor occurring in the prostate. Recently, the repetition rate is increasing. Image inspection method which we can check the prostate structure the most correctly is MRI(Magnetic Resonance Imaging), but it is hard to apply it to all the patients because of the cost. So, they use mostly TRUS(Transrectal Ultrasound) images acquired from prostate ultrasound inspection and which are cheap and easy to inspect the prostate in the process of treating and diagnosing the prostate cancer. Traditionally, in the hospital the doctors saw the TRUS images by their eyes and manually segmented the boundary between the prostate and nonprostate. But the manually segmenting process not only needed too much time but also had different boundaries according to the doctor. To cope the problems, some automatic segmentations of the prostate have been studied to generate the constant segmentation results and get the belief from patients. In this study, we propose an average shape model to segment the prostate boundary in TRUS prostate image. The method has 3 steps. First, it finds the probe using edge distribution. Next, it finds two straight lines connected with the probe. Finally it puts the shape model to the image using the position of the probe and straight lines.

TU‐F‐BRF‐02: MR‐US Prostate Registration Using Patient‐Specific Tissue Elasticity Property Prior for MR‐Targeted, TRUS‐Guided HDR Brachytherapy

Purpose:High‐dose‐rate (HDR) brachytherapy has become a popular treatment modality for prostate cancer. Conventional transrectal ultrasound (TRUS)‐guided prostate HDR brachytherapy could benefit significantly from MR‐targeted, TRUS‐guided procedure where the tumor locations, acquired from the multiparametric MRI, are incorporated into the treatment planning. In order to enable this integration, we have developed a MR‐TRUS registration with a patient‐specific biomechanical elasticity prior.Methods:The proposed method used a biomechanical elasticity prior to guide the prostate volumetric B‐spline deformation in the MRI and TRUS registration. The patient‐specific biomechanical elasticity prior was generated using ultrasound elastography, where two 3D TRUS prostate images were acquired under different probe‐induced pressures during the HDR procedure, which takes 2‐4 minutes. These two 3D TRUS images were used to calculate the local displacement (elasticity map) of two prostate volumes. The B‐spline transformation was calculated by minimizing the Euclidean distance between the normalized attribute vectors of the prostate surface landmarks on the MR and TRUS. This technique was evaluated through two studies: a prostate‐phantom study and a pilot study with 5 patients undergoing prostate HDR treatment. The accuracy of our approach was assessed through the locations of several landmarks in the post‐registration and TRUS images; our registration results were compared with the surface‐based method.Results:For the phantom study, the mean landmark displacement of the proposed method was 1.29±0.11 mm. For the 5 patients, the mean landmark displacement of the surface‐based method was 3.25±0.51 mm; our method, 1.71±0.25 mm. Therefore, our proposed method of prostate registration outperformed the surfaced‐based registration significantly.Conclusion:We have developed a novel MR‐TRUS prostate registration approach based on patient‐specific biomechanical elasticity prior. Successful integration of multi‐parametric MR and TRUS prostate images provides a prostate‐cancer map for treatment planning, enables accurate dose planning and delivery, and potentially enhances prostate HDR treatment outcome.

SU‐C‐WAB‐06: Deformable Registration of Post‐Implant MRI to Intra‐Operative Ultrasound Images for Permanent Prostate Brachytherapy Treatment Assessment

Purpose: Transrectal‐ultrasound (TRUS) is the most common image modality used in permanent prostate brachytherapy (PPI), while MRI images can provide additional anatomical information. Our goals were to develop a novel method to register post‐implant MRI to intra‐operative ultrasound (US) images and demonstrate its potential usage for retrospective implant assessment. Methods: TRUS images of prostate and non‐isocentric C‐arm fluoroscopy (FL) images are captured intraoperatively right after implant. The reconstructed 3D seed cloud from FL images (seeds_FL) will be used as a bridge to register post MRI to US images. The Registration of Ultrasound and Fluoroscopy (RUF) images is done by an intensity‐based point to volume algorithm. The day‐one post CT and T2‐MRI images are co‐registered and the 3D seed cloud is segmented from CT (seeds_CT). The iterative‐closest‐point algorithm is used to compute rigid transformation between seeds_FL and seed_CT. MRI can be transferred to FL coordinate using registration of two seed clouds and then to US coordinate using RUF registration. A thin‐plate‐spline algorithm is used to deform the MRI contours and images according to deformation of two seed clouds. To demonstrate this registration method, post MRI images from ten patients were registered to US images. The prostate contours were compared between post US and MRI. The planning needle interferences with critical structure like neurovascular bundles (NVB) were investigated. Results: After registration, center of mass of both prostate and urethra were within 3mm between two contour sets. The anterior boundaries of prostate were often overestimated in post US contours. When planning needles were superimposed over contours, there were about 3–4 needles passing through or in close vicinity of NVB in all patients. Conclusion: A novel method to register post‐implant MRI to intra‐operative US images was developed and demonstrated in evaluating accuracy of intra‐operative US contours and accessing needle passages to NVB during PPI.

WE‐C‐WAB‐11: Improved the Accuracy of Prostate Delineation for Ultrasound‐Guided CT‐Based Treatment Planning in Prostate HDR Brachytherapy: A Pilot Study with MRI Validation

Purpose: In CT‐based prostate High‐Dose‐Rate (HDR) brachytherapy, catheter (needle) placement is commonly performed under the guidance of intra‐operative transrectal ultrasound (TRUS), while treatment planning is based on post‐operative CT images. The main challenge for CT‐based treatment planning is to accurately define target (prostate) volume in CT images due to the poor soft‐tissue contrast. To overcome this limitation, we propose a novel approach that integrates intra‐operative ultrasound‐based prostate volume into treatment planning through TRUS‐CT fusion based on the catheter locations. Methods: Our approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 minutes. These TRUS images are then used to create prostate contours. The HDR catheters are reconstructed from the intra‐operative TRUS and post‐operative CT images, and subsequently used as landmarks for the TRUS‐CT image fusion using a fuzzy‐to‐deterministic algorithm. After TRUS‐CT fusion, the TRUS‐based prostate volume is deformed to the CT images for treatment planning. This technique was evaluated through two studies: a prostate‐phantom study and a pilot study with 9 patients undergoing HDR treatment for prostate cancer. The accuracy of our approach is assessed through the locations of 3 implanted fudicial (gold) markers, as well as previous T2‐weighted MR images of patients. Results: For the phantom study, the mean gold‐marker displacement was 0.41±0.11 mm. For the 9 patients, the mean gold‐marker displacement of was 1.09±0.25mm; the prostate volume difference between our approach and the MRI‐based volume was 6.99±0.68%, and the prostate volume Dice Overlap Coefficient was 92.66±1.17%. Conclusion: We have developed a novel approach to improve prostate contours utilizing intra‐operative TRUS‐based prostate volume in CT‐based prostate HDR treatment planning, demonstrated its clinical feasibility, and validated its accuracy with MRIs. This technique will improve prostate delineation, enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.

3-Dimensional Elastic Registration System of Prostate Biopsy Location by Real-Time 3-Dimensional Transrectal Ultrasound Guidance With Magnetic Resonance/Transrectal Ultrasound Image Fusion

We determined the accuracy of the novel Urostation 3-dimensional transrectal ultrasound system (Koelis, La Tranche, France) for image based mapping biopsies in a prostate phantom. The system is capable of 1) registering the 3-dimensional location of each biopsy track in the 3-dimensional prostate volume data and 2) performing elastic image fusion of transrectal ultrasound with magnetic resonance imaging. We used 3 CIRS-053 prostate phantoms containing 3 hypoechoic lesions to perform ultrasound guided biopsy and 3 CIRS-066 phantoms (Computerized Imaging Reference Systems, Norfolk, Virginia) containing 3 isoechoic but magnetic resonance imaging visible lesions to perform magnetic resonance fusion guided biopsy. Three targeted biopsies were done per lesion. Each biopsy tract was injected with gadolinium based magnetic resonance contrast mixed with india ink. Phantoms were then subjected to 1 mm slice magnetic resonance imaging and serial step sectioning to assess the accuracy of targeted biopsy. A total of 27 ultrasound guided biopsies were targeted into 9 hypoechoic lesions. All 27 biopsies (100%) successfully hit the target lesion. For hypoechoic lesions mean ± SD procedural targeting error was 1.52 ± 0.78 mm and system registration error was 0.83 mm, resulting in an overall error of 2.35 mm. Of the 27 magnetic resonance fusion biopsies 24 (84%) hit the lesion. For isoechoic lesions mean procedural targeting error was 2.09 ± 1.28 mm, resulting in an overall error of 2.92 mm. The novel, computer assisted, 3-dimensional transrectal ultrasound biopsy localization system achieved encouraging accuracy with less than 3 mm error for targeting hypoechoic and isoechoic lesions. The ability to register actual biopsy trajectory and perform elastic magnetic resonance/ultrasound image fusion is a significant advantage for future focal therapy application.

Fisher–Tippett Region-Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation

In this paper, a computerized approach to segmenting prostate lesions in transrectal ultrasound (TRUS) images is presented. The segmentation of prostate lesions from TRUS images is very challenging due to issues, such as poor contrast, low SNRs, and irregular shape variations. To address these issues, a novel approach is employed to segment the lesions from the surrounding prostate, where region merging is performed via a region-merging likelihood function based on regional statistics, as well as Fisher-Tippett statistics. Experimental results using TRUS prostate images demonstrate that the proposed Fisher-Tippett region-merging approach achieves more accurate segmentation of prostate lesions when compared to other segmentation methods.

Inter- and Intra-Observer Variability in Prostate Definition With Tissue Harmonic and Brightness Mode Imaging

The objective of this study was to compare the relative utility of tissue harmonic (H) and brightness (B) transrectal ultrasound (TRUS) images of the prostate by studying interobserver and intraobserver variation in prostate delineation. Ten patients with early-stage disease were randomly selected. TRUS images of prostates were acquired using B and H modes. The prostates on all images were contoured by an experienced radiation oncologist (RO) and five equally trained observers. The observers were blinded to information regarding patient and imaging mode. The volumes of prostate glands and areas of midgland slices were calculated. Volumes contoured were compared among the observers and between observer group and RO. Contours on one patient were repeated five times by four observers to evaluate the intraobserver variability. A one-sample Student t-test showed the volumes outlined by five observers are in agreement (p > 0.05) with the RO. Paired Student t-test showed prostate volumes (p = 0.008) and midgland areas (p = 0.006) with H mode were significantly smaller than that with B mode. Two-factor analysis of variances showed significant interobserver variability (p < 0.001) in prostate volumes and areas. Inter- and intraobserver consistency was quantified as the standard deviation of mean volumes and areas, and concordance indices. It was found that for small glands (≤35 cc) H mode provided greater interobserver consistency; however, for large glands (≥35 cc), B mode provided more consistent estimates. H mode provided superior inter- and intraobserver agreement in prostate volume definition for small to medium prostates. In large glands, H mode does not exhibit any additional advantage. Although harmonic imaging has not proven advantageous for all cases, its utilization seems to be judicious for small prostates.

Image Registration Accuracy of a 3-Dimensional Transrectal Ultrasound-Guided Prostate Biopsy System

For a follow-up prostate biopsy procedure, it is useful to know the previous biopsy locations in anatomic relation to the current transrectal ultrasound (TRUS) scan. The goal of this study was to validate the performance of a 3-dimensional TRUS-guided prostate biopsy system that can accurately relocate previous biopsy sites. To correlate biopsy locations from a sequence of visits by a patient, the prostate surface data obtained from a previous visit needs to be registered to the follow-up visits. Two interpolation methods, thin-plate spline (TPS) and elastic warping (EW), were tested for registration of the TRUS prostate image to follow-up scans. We validated our biopsy system using a custom-built phantom. Beads were embedded inside the phantom and were located in each TRUS scan. We recorded the locations of the beads before and after pressures were applied to the phantom and then compared them with computer-estimated positions to measure performance. In our experiments, before system processing, the mean target registration error (TRE) +/- SD was 6.4 +/- 4.5 mm (range, 3-13 mm). After registration and TPS interpolation, the TRE was 5.0 +/- 1.03 mm (range, 2-8 mm). After registration and EW interpolation, the TRE was 2.7 +/- 0.99 mm (range, 1-4 mm). Elastic warping was significantly better than the TPS in most cases (P < .0011). For clinical applications, EW can be implemented on a graphics processing unit with an execution time of less than 2.5 seconds. Elastic warping interpolation yields more accurate results than the TPS for registration of TRUS prostate images. Experimental results indicate potential for clinical application of this method.

TH‐C‐304A‐06: Assessment of Registration Accuracy in 3D Transrectal Ultrasound Images of Prostates

Purpose: To evaluate the accuracy of our rigid and non‐rigid registration methods to align three‐dimensional (3D) transrectal ultrasound (TRUS) images of the prostate in order to improve repeat biopsy planning and guidance for prostate cancer diagnosis. Methods and Materials: Three 3D prostate ultrasound images were obtained from each of the 16 patients during 3D prostate ultrasound‐guided biopsy procedures. These 3D images had identifiable calcifications and cysts, which were used as fiducials for the registration validation. For each prostate, images at two different time points (to and t1) during the procedure were selected and the 3D coordinates of the identified fiducials were recorded. A total of 62 fiducials were identified. All images were then segmented manually and semi‐automatically. Images at to were registered to the corresponding images at t1 using rigid and non‐rigid registration. Rigid and non‐rigid registration was performed using boundary‐based iterative closest point and thin‐plate spline algorithms, respectively. We evaluated the registration methods by determining the target registration error (TRE), effect of segmentation variability on registration variability, the TRE dependence on fiducial distance from the transducer tip, and the fiducial localization error (FLE). Results: Tests showed that the FLE was 1.3mm. The mean TRE for manual and semi‐automatic segmentation was 2.2mm and 2.3mm respectively in both rigid and non‐rigid cases. There was little correlation between the TRE and the distance of the fiducials from the ultrasound transducer tip, and segmentation variability resulted in a 0.57mm standard deviation of the TRE. Conclusions: Our results suggest that both rigid and non‐rigid registration techniques for 3D TRUS images during a biopsy procedure are accurate to within 2.3mm, which is less than the 5mm radius of the smallest tumors considered clinically significant. Future tests will evaluate registration accuracy for multimodal cases, specifically magnetic resonance to ultrasound images.

Application of reinforcement learning for segmentation of transrectal ultrasound images

BackgroundAmong different medical image modalities, ultrasound imaging has a very widespread clinical use. But, due to some factors, such as poor image contrast, noise and missing or diffuse boundaries, the ultrasound images are inherently difficult to segment. An important application is estimation of the location and volume of the prostate in transrectal ultrasound (TRUS) images. For this purpose, manual segmentation is a tedious and time consuming procedure.MethodsWe introduce a new method for the segmentation of the prostate in transrectal ultrasound images, using a reinforcement learning scheme. This algorithm is used to find the appropriate local values for sub-images and to extract the prostate. It contains an offline stage, where the reinforcement learning agent uses some images and manually segmented versions of these images to learn from. The reinforcement agent is provided with reward/punishment, determined objectively to explore/exploit the solution space. After this stage, the agent has acquired knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images to extract a coarse version of the prostate.ResultsWe have carried out experiments to segment TRUS images. The results demonstrate the potential of this approach in the field of medical image segmentation.ConclusionBy using the proposed method, we can find the appropriate local values and segment the prostate. This approach can be used for segmentation tasks containing one object of interest. To improve this prototype, more investigations are needed.

3D prostate model formation from non-parallel 2D ultrasound biopsy images

Biopsy of the prostate using 2D transrectal ultrasound (TRUS) guidance is the current gold standard for diagnosis of prostate cancer; however, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. We propose a technique for patient-specific 3D prostate model reconstruction from a sparse collection of non-parallel 2D TRUS biopsy images. Our method conforms to the restrictions of current TRUS biopsy equipment and could be efficiently incorporated into current clinical biopsy procedures for needle guidance without the need for expensive hardware additions. In this paper, the model reconstruction technique is evaluated using simulated biopsy images from 3D TRUS prostate images of 10 biopsy patients. All reconstructed models are compared to their corresponding 3D manually segmented prostate models for evaluation of prostate volume accuracy and surface errors (both regional and global). The number of 2D TRUS biopsy images used for prostate modeling was varied to determine the optimal number of images necessary for accurate prostate surface estimation.