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Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (

PURPOSE 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. METHODS 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. RESULTS 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

A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 $\pm$ 0.81 mm and 2.81 $\pm$ 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

In this study, we proposed an efficient nonrigid magnetic resonance (MR) to transrectal ultrasound (TRUS) deformable registration method in order to improve the accuracy of targeting suspicious regions during a three dimensional (3-D) TRUS guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighborhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization-based algorithmic scheme was introduced to extract the deformations and align the two MIND descriptors. The registration accuracy was evaluated using 20 patient images by calculating the TRE using manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone. Additional performance metrics [Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD)] were also calculated by comparing the MR and TRUS manually segmented prostate surfaces in the registered images. Experimental results showed that the proposed method yielded an overall median TRE of 1.76 mm. The results obtained in terms of DSC showed an average of $80.8\pm7.8\hbox{\%}$ for the apex of the prostate, $92.0\pm3.4\hbox{\%}$ for the mid-gland, $81.7\pm6.4\hbox{\%}$ for the base and $85.7\pm4.7\hbox{\%}$ for the whole gland. The surface distance calculations showed an overall average of $1.84\pm0.52$ mm for MAD and $6.90\pm2.07$ mm for MAXD.

The purpose of this article is to compare transrectal ultrasound (TRUS) biopsy accuracies of operators with different levels of prostate MRI experience using cognitive registration versus MRI-TRUS fusion to assess the preferred method of TRUS prostate biopsy for MRI-identified lesions. SUBJECTS AND METHODS; One hundred patients from a prospective prostate MRI-TRUS fusion biopsy study were reviewed to identify all patients with clinically significant prostate adenocarcinoma (PCA) detected on MRI-targeted biopsy. Twenty-five PCA tumors were incorporated into a validated TRUS prostate biopsy simulator. Three prostate biopsy experts, each with different levels of experience in prostate MRI and MRI-TRUS fusion biopsy, performed a total of 225 simulated targeted biopsies on the MRI lesions as well as regional biopsy targets. Simulated biopsies performed using cognitive registration with 2D TRUS and 3D TRUS were compared with biopsies performed under MRI-TRUS fusion.Two-dimensional and 3D TRUS sampled only 48% and 45% of clinically significant PCA MRI lesions, respectively, compared with 100% with MRI-TRUS fusion. Lesion sampling accuracy did not statistically significantly vary according to operator experience or tumor volume. MRI-TRUS fusion-naïve operators showed consistent errors in targeting of the apex, midgland, and anterior targets, suggesting that there is biased error in cognitive registration. The MRI-TRUS fusion expert correctly targeted the prostate apex; however, his midgland and anterior mistargeting was similar to that of the less-experienced operators.MRI-targeted TRUS-guided prostate biopsy using cognitive registration appears to be inferior to MRI-TRUS fusion, with fewer than 50% of clinically significant PCA lesions successfully sampled. No statistically significant difference in biopsy accuracy was seen according to operator experience with prostate MRI or MRI-TRUS fusion.

Background To develop and optimize radiofrequency (RF) hardware for the detection of endogenous sodium (23Na) by 3.0 Tesla (T) MRI in the human prostate. Methods A transmit-only receive-only (TORO) RF system of resonators consisting of an unshielded, asymmetric, quadrature birdcage (transmit), and an endorectal (ER), linear, surface (receive) coil were developed and tested on a 3T MRI scanner. Two different ER receivers were constructed; a single-tuned (23Na) and a dual-tuned (1H/23Na). Both receivers were evaluated by the measurements of signal-to-noise ratio (SNR) and B1 homogeneity. For tissue sodium concentration (TSC) quantification, vials containing known sodium concentrations were incorporated into the ER. The system was used to measure the prostate TSC of three men (age 55 ± 5 years) with biopsy-proven prostate cancer. Results B1 field inhomogeneity of the asymmetric transmitter was estimated to be less than 5%. The mean SNR measured in a region of interest within the prostate using the single-tuned ER coil was 54.0 ± 4.6. The mean TSC in the central gland was 60.2 ± 5.7 mmol/L and in the peripheral gland was 70.5 ± 9.0 mmol/L. Conclusion A TORO system was developed and optimized for 23Na MRI of the human prostate which showed good sensitivity throughout the prostate for quantitative measurement of TSC. J. Magn. Reson. Imaging 2015;42:436–445.

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

RRP radical retropubic prostatectomy, HDR high dose rate brachytherapy, Cryo cryoablation, HIFU high-intensity focused ultrasound, TURP transurethral resection of the prostate, OIU optic internal urethrotomy, SD standard deviation, IIEF-5 5-item version of the international index of erectile function, IPSS international prostate symptom score, QoL quality of life *Erectile dysfunction and Mean scores for IIEF-5 were restricted to those men who reported IIEF-5 17 at baseline Vol. 195, No. 4S, Supplement, Friday, May 6, 2016 THE JOURNAL OF UROLOGY e201

Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)≥3+3, and AUC of 0.89 for those with GSs≥3+4.

To develop and evaluate a technique for the registration of in vivo prostate magnetic resonance (MR) images to digital histopathologic images by using image-guided specimen slicing based on strand-shaped fiducial markers relating specimen imaging to histopathologic examination.The study was approved by the institutional review board (the University of Western Ontario Health Sciences Research Ethics Board, London, Ontario, Canada), and written informed consent was obtained from all patients. This work proposed and evaluated a technique utilizing developed fiducial markers and real-time three-dimensional visualization in support of image guidance for ex vivo prostate specimen slicing parallel to the MR imaging planes prior to digitization, simplifying the registration process. Means, standard deviations, root-mean-square errors, and 95% confidence intervals are reported for all evaluated measurements.The slicing error was within the 2.2 mm thickness of the diagnostic-quality MR imaging sections, with a tissue block thickness standard deviation of 0.2 mm. Rigid registration provided negligible postregistration overlap of the smallest clinically important tumors (0.2 cm(3)) at histologic examination and MR imaging, whereas the tested nonrigid registration method yielded a mean target registration error of 1.1 mm and provided useful coregistration of such tumors.This method for the registration of prostate digital histopathologic images to in vivo MR images acquired by using an endorectal receive coil was sufficiently accurate for coregistering the smallest clinically important lesions with 95% confidence.

Fetal intra-abdominal umbilical vein varix is a rare condition characterized by focal dilatation of the umbilical vein of the fetus. We report a case of fetal intra-abdominal umbilical vein varix associated with additional sonographic abnormalities and detected at 31 weeks' gestation. Several follow-up sonographic examinations were performed, and the prenatal findings were confirmed on postnatal ultrasound. The diagnosis was facilitated by the use of three-dimensional power Doppler sonography.

Purpose: 3D-TRUS-guided prostate biopsy permits a 3D record of biopsy cores, supporting the planning of targets to resample or avoid during repeat biopsy sessions. Image registration is required in order to map biopsy targets planned on a previous session’s 3D-TRUS image into the context of the current session. The authors evaluated the performance of surface- and intensity-based rigid and nonrigid registration algorithms for this task using a clinically motivated success criterion of a maximum 2.5 mm target registration error (TRE). Methods: The authors collected two 3D-TRUS images for each of 13 patients, where each image was collected in a separate biopsy session, and the sessions were 1 week apart. The authors tested the iterative closest point and thin-plate spline surface-based registration methods, and the block matching and B-spline intensity-based methods. Manually marked intrinsic fiducials (calcifications) were used to calculate a TRE for each of the tested methods. In addition, error ellipsoids, anisotropy, and variability due to image segmentation were analyzed. All analysis was performed separately for the peripheral zone since this area harbors up to 80% of all prostate cancer. Results: Only the intensity-based nonrigid registration method met the success criterion for both the whole gland and the peripheral zone. Segmentation was a substantial contributor to registration error variability for the surface-based methods, and the surface-based methods resulted in greater error volumes and anisotropy. Conclusions: Intensity-based rigid registration is clinically sufficient to register regions outside the peripheral zone, but nonrigid registration is required in order to register the peripheral zone with clinically needed accuracy. The clinical advantage of using nonrigid registration is questionable since the difference between the RMS TREs for rigid and nonrigid intensity-based registration could be considered to be small (0.3 mm) and is statistically significant. If the added clinical value in performing a nonrigid registration is insufficient given the additional time required for this computation, rigid registration alone may be suitable.

Purpose: Prostate biopsy is the clinical standard for the definitive diagnosis of prostate cancer. To overcome the limitations of 2D TRUS-guided biopsy systems when targeting preplanned locations, systems have been developed with 3D guidance to improve the accuracy of cancer detection. Prostate deformation due to needle insertion and biopsy gun firing is a potential source of error that can cause target misalignments during biopsies. Methods: The authors used nonrigid registration of 2D TRUSimages to quantify the deformation that occurs during the needle insertion and the biopsy gun firing procedure and compare this effect in biopsies performed using a hand-held TRUS probe to those performed using a mechanically assisted 3D TRUS-guided biopsy system. The authors calculated a spatially varying 95% confidence interval on the prostate tissue motion and analyzed this motion both as a function of distance to the biopsy needle and as a function of distance to the lower piercing point of the prostate. The former is relevant because biopsy targets lie along the needle axis, and the latter is of particular importance due to the reported high concentration of prostate cancer in the peripheral zone, a substantial portion of which lies on the posterior side of the prostate where biopsy needles enter the prostate after penetrating the rectal wall during transrectal biopsy. Results: The results show that for both systems, the tissue deformation is such that throughout the length of the needle axis, including regions proximal to the lower piercing point, spherical tumors with a radius of 2.1 mm or more can be sampled with 95% confidence under the assumption of zero error elsewhere in the biopsy system. More deformation was observed in the direction orthogonal to the needle axis compared to the direction parallel to the needle axis; this is of particular importance given the long, narrow shape of the biopsy core. The authors measured lateral tissue motion proximal to the needle axis of not more than 1.5 mm, with 95% confidence. The authors observed a statistically significant and clinically insignificant maximum difference of 0.38 mm in the deformation, resulting from the hand-held and mechanically assisted systems along the needle axis, and the mechanical system resulted in a lower relative increase in deformation proximal to the needle axis during needle insertion, as well as lower variability of deformation during biopsy gun firing. Conclusions: Given the clinical need to biopsy tumors of volume greater than or equal to 0.5 cm 3 , corresponding to spherical tumors with a radius of 5 mm or more, the tissue motion induced by needle insertion and gun firing is an important consideration when setting the design specifications for TRUS-guided prostate biopsy systems.

PURPOSE: To analyze the effectiveness and complication rate of ultrasound (US)-guided perforation and lavage using a two-needle technique with 16 - 18 G needles in the treatment of patients with calcific tendinopathy in the shoulder, elbow, hip, and knee by radiological and clinical follow-up. MATERIALS AND METHODS: A retrospective chart review was performed and 40 patients (13 male, 27 female; mean age, 53.5 years; range 24 -74 years) were identified as having received US-guided perforation and lavage due to symptomatic calcific tendinopathy of the rotator cuff tendons, triceps, extensor and flexor tendons at the elbow, rectus femoris tendon and patellar tendons. The radiographic outcome was assessed by comparison of the size and quality of the calcification before and 6 weeks after the procedure. On US images, the quality of the acoustic shadow was assessed, together with other alterations of the tendon and surrounding tissue. Patients were interviewed by telephone to assess the clinical outcome regarding pre-treatment and post-treatment pain and tendon function. RESULTS: 34 shoulder tendons and 6 non-shoulder tendons were identified. The mean calcium reduction was 39.9 mm 2 (range, 0 - 215; p < 0.001), while 80 % of patient showed a resolution of more than 60 % resulting in good clinical improvement. A very low complication rate was found (1 partial tear). CONCLUSION: The US-guided perforation and lavage technique is an effective and safe treatment for rotator cuff calcifications as well as for other body tendons. Although the two-needle technique and large needles were used in this study, a very low complication rate was detected.

Purpose: Automatic identification of the biopsy-core tissue location during a prostate biopsy procedure would provide verification that targets were adequately sampled and would allow for appropriate intraprocedure biopsy target modification. Localization of the biopsy core requires accurate segmentation of the biopsy needle and needle tip from transrectal ultrasound(TRUS) biopsy images. A temporal-based TRUS needle segmentation algorithm was developed specifically for the prostate biopsy procedure to automatically identify the TRUSimage containing the biopsy needle from a collection of 2D TRUSimages and to segment the biopsy-core location from the 2D TRUSimage. Methods: The temporal-based segmentation algorithm performs a temporal analysis on a series of biopsy TRUSimages collected throughout needle insertion and withdrawal. Following the identification of points of needle insertion and retraction, the needle axis is segmented using a Hough transform-based algorithm, which is followed by a temporospectral TRUS analysis to identify the biopsy-needle tip. Validation of the temporal-based algorithm is performed on 108 TRUS biopsy sequences collected from the procedures of ten patients. The success of the temporal search to identify the proper images was manually assessed, while the accuracies of the needle-axis and needle-tip segmentations were quantitatively compared to implementations of two other needle segmentation algorithms within the literature. Results: The needle segmentation algorithm demonstrated a > 99 % accuracy in identifying the TRUSimage at the moment of needle insertion from the collection of real-time TRUSimages throughout the insertion and withdrawal of the biopsy needle. The segmented biopsy-needle axes were accurate to within 2.3 ± 2.0 ° and 0.48 ± 0.42 mm of the gold standard. Identification of the needle tip to within half of the biopsy-core length ( 10 mm ) was 95% successful with a mean error of 2.4 ± 4.0 mm . Needle-tip detection using the temporal-based algorithm was significantly more accurate ( p 0.001 ) than the other two algorithms tested, while the segmentation of the needle axis was not significantly different between the three algorithms. Conclusions: The temporal-based needle segmentation algorithm accurately segments the location of the biopsy core from 2D TRUSimages of clinical prostate biopsy procedures. The results for needle-tip localization demonstrated that the temporal-based algorithm is significantly more accurate than implementations of some existing needle segmentation algorithms within the literature.

To compare the accuracy of biopsy with two-dimensional (2D) transrectal ultrasonography (US) with that of biopsy with conventional three-dimensional (3D) transrectal US and biopsy with guided 3D transrectal US in the guidance of repeat prostate biopsy procedures in a prostate biopsy simulator.The institutional review board approved this retrospective study. Five residents and five experts performed repeat biopsies with a biopsy simulator that contained the transrectal US prostate images of 10 patients who had undergone biopsy. Simulated repeat biopsies were performed with 2D transrectal US, conventional 3D transrectal US, and guided 3D transrectal US (an extension of 3D transrectal US that enables active display of biopsy targets). The modalities were compared on the basis of time per biopsy and how accurately simulated repeat biopsies could be guided to specific targets. The probability for successful biopsy of a repeat target was calculated for each modality.Guided 3D transrectal US was significantly (P.01) more accurate for simulated biopsy of repeat targets than was 2D or 3D transrectal US, with a biopsy accuracy of 0.86 mm +/- 0.47 (standard deviation), 3.68 mm +/- 2.60, and 3.60 mm +/- 2.57, respectively. Experts had a 70% probability of sampling a prior biopsy target volume of 0.5 cm(3) with 2D transrectal US; however, the probability approached 100% with guided 3D transrectal US. Biopsy accuracy was not significantly different between experts and residents for any modality; however, experts were significantly (P.05) faster than residents with each modality.Repeat biopsy of the prostate with 2D transrectal US has limited accuracy. Compared with 2D transrectal US, the biopsy accuracy of both experts and residents improved with guided 3D transrectal US but did not improve with conventional 3D transrectal US.