Transrectal Ultrasound Images Research Articles

The learning curve and experience of a novel multi-modal image fusion targeted transperineal prostate biopsy technique using electromagnetic needle tracking under local anesthesia.

Prostate cancer is the most common malignant tumor of male genitourinary system, and the gold standard for its diagnosis is prostate biopsy. Focusing on the methods and skills of prostate biopsy, we explored the learning curve and experience of a novel magnetic resonance imaging and transrectal ultrasound (mpMRI-TRUS) image fusion transperineal biopsy (TPB) technique using electromagnetic needle tracking under local anesthesia. The clinical and pathological data of 92 patients who underwent targeted TPB from January 2023 to July 2023 in our center were prospectively collected. The cumulative sum (CUSUM) analysis method and the best fitting curve were used to analyze the learning curve of this novel technique, and the clinical characteristics, perioperative data and tumor positive rate of prostate biopsy of patients at different stages of the learning curve were compared. With the increase of the number of surgical cases, the overall operative time showed a downward trend. The best fitting curve of CUSUM reached its peak at the twelfth case, which is the minimum cumulative number of surgical cases needed to cross the learning curve of the operation. Taking this as the boundary, the learning curve is divided into two stages: learning improvement stage (group A, 12 cases) and proficiency stage (group B, 80 cases). The surgical time and visual analog scale score during prostate biopsy in group A were significantly higher than those in group B. The visual numerical scale score during prostate biopsy in group A was significantly lower than that in group B. There was no statistically significant difference between group A and group B in the detection rate of csPCa and the incidence of perioperative complications. The novel targeted TPB technique is divided into learning improvement stage and proficiency stage, and 12 cases may be the least cumulative number.

Transrectal ultrasound for intraoperative interstitial needle guidance in cervical cancer brachytherapy.

This study aimed to prospectively assess the visibility of interstitial needles on transrectal ultrasound (TRUS) in cervical cancer brachytherapy patients and evaluate its impact on implant and treatment plan quality. TRUS was utilized during and after applicator insertion, with each needle's visibility documented through axial images at the high-risk clinical target volume's largest diameter. Needle visibility on TRUS was scored from 0 (no visibility) to 3 (excellent discrimination, margins distinct). Quantitative assessment involved measuring the distance between tandem and each needle on TRUS and comparing it to respective magnetic resonance imaging (MRI) measurements. Expected treatment plan quality based on TRUS images was rated from 1 (meeting all planning objectives) to 4 (violation of High-risk clinical target volume (CTVHR) and/or organ at risk (OAR) hard constraints) and compared to the final MRI-based plan. Analysis included 23 patients with local FIGO stage IB2-IVA, comprising 41 applications with a total of 230 needles. A high visibility rate of 99.1% (228/230 needles) was observed, with a mean visibility score of 2.5 ± 0.7 for visible needles. The maximum and mean difference between MRI and TRUS measurements were 8 mm and -0.1 ± 1.6 mm, respectively, with > 3 mm discrepancies in 3.5% of needles. Expected treatment plan quality after TRUS assessment exactly aligned with the final MRI plan in 28 out of 41 applications with only minor deviations in all other cases. Real-time TRUS-guided interstitial needle placement yielded high-quality implants, thanks to excellent needle visibility during insertion. This supports the potential of TRUS-guided brachytherapy as a promising modality for gynecological indications.

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary diagnosis. In response, we introduce the novel Deep Augmented Metric Learning (DAML) network, specifically tailored for ultrasound-based prostate cancer classification. The DAML network represents a significant innovation in the metric learning space, introducing the Semantic Differences Mining Strategy (SDMS) to effectively discern and represent subtle differences in prostate ultrasound images, thereby enhancing tumor classification accuracy. Additionally, the DAML network strategically addresses class variability and limited sample sizes by combining the Linear Interpolation Augmentation Strategy (LIAS) and Permutation-Aided Reconstruction Loss (PARL). This approach enriches feature representation and introduces variability with straightforward structures, mirroring the efficacy of advanced sample generation techniques. We carried out comprehensive empirical assessments of the DAML model by testing its key components against a range of models, ensuring its effectiveness. Our results demonstrate the enhanced performance of the DAML model, achieving classification accuracies of 0.857 and 0.888 for benign and malignant cancers, respectively, underscoring its effectiveness in prostate cancer classification via medical imaging.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer.

We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

Prediction of Gleason score in prostate cancer patients based on radiomic features of transrectal ultrasound images.

The aim of this study was to develop a model for predicting the Gleason score of patients with prostate cancer based on ultrasound images. Transrectal ultrasound images of 838 prostate cancer patients from The Cancer Imaging Archive database were included in this cross-section study. Data were randomly divided into the training set and testing set (ratio 7:3). A total of 103 radiomic features were extracted from the ultrasound image. Lasso regression was used to select radiomic features. Random forest and broad learning system (BLS) methods were utilized to develop the model. The area under the curve (AUC) was calculated to evaluate the model performance. After the screening, 10 radiomic features were selected. The AUC and accuracy of the radiomic feature variables random forest model in the testing set were 0.727 (95% CI, 0.694-0.760) and 0.646 (95% CI, 0.620-0.673), respectively. When PSA and radiomic feature variables were included in the random forest model, the AUC and accuracy of the model were 0.770 (95% CI, 0.740-0.800) and 0.713 (95% CI, 0.688-0.738), respectively. While the BLS method was utilized to construct the model, the AUC and accuracy of the model were 0.726 (95% CI, 0.693-0.759) and 0.698 (95% CI, 0.673-0.723), respectively. In predictions for different Gleason grades, the highest AUC of 0.847 (95% CI, 0.749-0.945) was found to predict Gleason grade 5 (Gleason score ≥9). A model based on transrectal ultrasound image features showed a good ability to predict Gleason scores in prostate cancer patients. This study used ultrasound-based radiomics to predict the Gleason score of patients with prostate cancer.

Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden’s index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

Transrectal ultrasound vector projectile imaging for time-resolved visualization of flow dynamics in the male urethra: A clinical pilot study.

Quantitative and comprehensive visualization of urinary flow dynamics in the urethra is crucial for investigating patient-specific mechanisms of lower urinary tract symptoms (LUTS). Although some methods can evaluate the global properties of the urethra, it is critical to assess the local information, such as the location of the responsible lesion and its interactions with urinary flow in relation to LUTS. This approach is vital for enhancing personalized and focal treatments. However, there is a lack of such diagnostic tools that can directly observe how the urethral shape and motion impact urinary flow in the urethra. This study aimed to develop a novel transrectal ultrasound imaging modality based on the contrast-enhanced urodynamic vector projectile imaging (CE-UroVPI) framework and validate its clinical applicability for visualizing time-resolved flow dynamics in the urethra. A new CE-UroVPI system was developed using a research-purpose ultrasound platform and a custom transrectal linear probe, and an imaging protocol for acquiring urodynamic echo data in male patients was designed. Thirty-four male patients with LUTS participated in this study. CE-UroVPI was performed to acquire ultrasound echo signals from the participant's urethra and urinary flow at various voiding phases (initiation, maintenance, and terminal). The ultrasound datasets were processed with custom software to visualize urinary flow dynamics and urethra tissue deformation. The transrectal CE-UroVPI system successfully visualized the time-resolved multidirectional urinary flow dynamics in the prostatic urethra during the initiation, maintenance, and terminal phases of voiding in 17 patients at a frame rate of 1250 fps. The maximum flow speed measured in this study was 2.5m/s. In addition, when the urethra had an obstruction or an irregular partial deformation, the devised imaging modality visualized complex flow patterns, such as vortices and flow jets around the lesion. Our study findings demonstrate that the transrectal CE-UroVPI system developed in this study can effectively image fluid-structural interactions in the urethra. This new diagnostic technology has the potential to facilitate quantitative and precise assessments of urethral voiding functions and aid in the improvement of focal and effective treatments for patients with LUTS.

Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging.

This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa). A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve. The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort. CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.

Joint task semi-supervised semantic segmentation for TRUS image

Transrectal ultrasound (TRUS) is an important tool for prostate observation, playing a crucial role in medical processes such as multimodal image registration and surgical planning. However, TRUS images are often plagued by blurry boundary information and artifacts, making manual annotation a time-consuming and labor-intensive task, which results in insufficient pixel-labeled data for training neural networks. Therefore, this paper introduces a semi-supervised neural network based on joint tasks for TRUS image segmentation, which combines confidence information from target localization and semantic segmentation to generate pseudo-labels for collectively training unlabeled samples. Additionally, this method supports the generation of pseudo-labels using only location information for weakly supervised training of neural networks. Compared to other state-of-the-art models, our results show that this approach outperforms in segmentation performance with a smaller dataset, achieving an mean Dice similarity coefficient and mean Jaccard similarity coefficient of 92.8 % and 88.4 %, respectively. Furthermore, the experiments reveal that reducing the coupling of two decoders' parameters between the segmentation and detection tasks improves segmentation accuracy as dataset size decreases.

Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles.

Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is required on transrectal ultrasound (TRUS) images during prostate brachytherapy procedures. Implanted needles cause artifacts that may make this task difficult and time-consuming. Thus, previous studies have focused on the simpler problem of segmentation in the absence of needles at the cost of reduced clinical utility. To use a convolutional neural network (CNN) algorithm for segmentation of the prostatic CTV in TRUS images post-needle insertion obtained from prostate brachytherapy procedures to better meet the demands of the clinical procedure. A dataset consisting of 144 3-dimensional (3D) TRUS images with implanted metal brachytherapy needles and associated manual CTV segmentations was used for training a 2-dimensional (2D) U-Net CNN using a Dice Similarity Coefficient (DSC) loss function. These were split by patient, with 119 used for training and 25 reserved for testing. The 3D TRUS training images were resliced at radial (around the axis normal to the coronal plane) and oblique angles through the center of the 3D image, as well as axial, coronal, and sagittal planes to obtain 3689 2D TRUS images and masks for training. The network generated boundary predictions on 300 2D TRUS images obtained from reslicing each of the 25 3D TRUS images used for testing into 12 radial slices (15° apart), which were then reconstructed into 3D surfaces. Performance metrics included DSC, recall, precision, unsigned and signed volume percentage differences (VPD/sVPD), mean surface distance (MSD), and Hausdorff distance (HD). In addition, we studied whether providing algorithm-predicted boundaries to the physicians and allowing modifications increased the agreement between physicians. This was performed by providing a subset of 3D TRUS images of five patients to five physicians who segmented the CTV using clinical software and repeated this at least 1week apart. The five physicians were given the algorithm boundary predictions and allowed to modify them, and the resulting inter- and intra-physician variability was evaluated. Median DSC, recall, precision, VPD, sVPD, MSD, and HD of the 3D-reconstructed algorithm segmentations were 87.2 [84.1, 88.8]%, 89.0 [86.3, 92.4]%, 86.6 [78.5, 90.8]%, 10.3 [4.5, 18.4]%, 2.0 [-4.5, 18.4]%, 1.6 [1.2, 2.0]mm, and 6.0 [5.3, 8.0]mm, respectively. Segmentation time for a set of 12 2D radial images was 2.46 [2.44, 2.48]s. With and without U-Net starting points, the intra-physician median DSCs were 97.0 [96.3, 97.8]%, and 94.4 [92.5, 95.4]% (p<0.0001), respectively, while the inter-physician median DSCs were 94.8 [93.3, 96.8]% and 90.2 [88.7, 92.1]%, respectively (p<0.0001). The median segmentation time for physicians, with and without U-Net-generated CTV boundaries, were 257.5 [211.8, 300.0]s and 288.0 [232.0, 333.5]s, respectively (p=0.1034). Our algorithm performed at a level similar to physicians in a fraction of the time. The use of algorithm-generated boundaries as a starting point and allowing modifications reduced physician variability, although it did not significantly reduce the time compared to manual segmentations.

Automatic search for photoacoustic marker using automated transrectal ultrasound.

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [IEEE Robot. Autom. Lett8, 1287 (2023).10.1109/LRA.2022.3191788]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53°±0.30° average error after 9 data acquisitions, while the TS and GSS algorithm can achieve and 0.48°±0.32° average errors after 28 data acquisitions.

Organ boundary delineation for automated diagnosis from multi-center using ultrasound images

Delineation of prostate boundary is a critical procedure in multi-modal prostate registration and patient-specific anatomical modeling for surgical planning and image-guided biopsy of prostate cancer. Accurate segmentation of the prostate from medical transrectal ultrasound (TRUS) images is challenging due to the presence of shadow artifacts that may cause missing or ambiguous boundaries of the prostate. In this work, we developed a novel hybrid model consisting of four primary merits to address this issue: (1) integrating the properties of the principal curve to fit the data center intelligently and of the artificial intelligence model to decrease model error; (2) developing a principal curve-based adaptive polygon tracking model to acquire the vertices sequence, which is designed via newly merging an automatic judgment of data radius algorithm; (3) introducing the quantum characteristics into the historical storage-based evolutionary model, which is proposed for the first attempt, whilst innovatively introduced new mutation technique and cuckoo search model; (4) presenting an interpretable mathematical model (explained via parameters of neural network) to express the smooth boundary. Promising experimental outcomes denote that our model has satisfactory segmentation performance. Future work will concentrate on improving the trustworthy performance of the model.

Boundary delineation in transrectal ultrasound images for region of interest of prostate

Accurate and robust prostate segmentation in transrectal ultrasound (TRUS) images is of great interest for ultrasound-guided brachytherapy for prostate cancer. However, the current practice of manual segmentation is difficult, time-consuming, and prone to errors. To overcome these challenges, we developed an accurate prostate segmentation framework (A-ProSeg) for TRUS images. The proposed segmentation method includes three innovation steps: (1) acquiring the sequence of vertices by using an improved polygonal segment-based method with a small number of radiologist-defined seed points as prior points; (2) establishing an optimal machine learning-based method by using the improved evolutionary neural network; and (3) obtaining smooth contours of the prostate region of interest using the optimized machine learning-based method. The proposed method was evaluated on 266 patients who underwent prostate cancer brachytherapy. The proposed method achieved a high performance against the ground truth with a Dice similarity coefficient of 96.2% ± 2.4%, a Jaccard similarity coefficient of 94.4% ± 3.3%, and an accuracy of 95.7% ± 2.7%; these values are all higher than those obtained using state-of-the-art methods. A sensitivity evaluation on different noise levels demonstrated that our method achieved high robustness against changes in image quality. Meanwhile, an ablation study was performed, and the significance of all the key components of the proposed method was demonstrated.

Observer preference of artificial intelligence-generated versus clinical prostate contours for ultrasound-based high dose rate brachytherapy.

For trans-rectal ultrasound (TRUS)-based high dose rate (HDR) prostate brachytherapy, prostate contouring can be challenging due to artifacts from implanted needles, bleeding, and calcifications. To evaluate the geometric accuracy and observer preference of an artificial intelligence (AI) algorithm for generating prostate contours on TRUS images with implanted needles. We conducted a retrospective study of 150 patients, who underwent HDR brachytherapy. These patients were randomly divided into training (104), validation (26) and testing (20) sets. An AI algorithm was trained/validated utilizing the TRUS image and reference (clinical) contours. The algorithm then provided contours for the test set. For evaluation, we calculated the Dice coefficient between AI and reference prostate contours. We then presented AI and reference contours to eight clinician observers, and asked observers to select their preference. Observers were blinded to the source of contours. We calculated the percentage of cases in which observers preferred AI contours. Lastly, we evaluate whether the presence of AI contours improved the geometric accuracy of prostate contours provided by five resident observers for a 10-patient subset. The median Dice coefficient between AI and reference contours was 0.92 (IQR: 0.90-0.94). Observers preferred AI contours for a median of 57.5% (IQR: 47.5, 65.0) of the test cases. For resident observers, the presence of AI contours was associated with a 0.107 (95% CI: 0.086, 0.128; p<0.001) improvement in Dice coefficient for the 10-patient subset. The AI algorithm provided high-quality prostate contours on TRUS with implanted needles. Further prospective study is needed to better understand how to incorporate AI prostate contours into the TRUS-based HDR brachytherapy workflow.

Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy.

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. 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. 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 ± 0.29mm from the experimental evaluation). The result is also comparable with our previous approach (i.e., 1.05 ± 0.37mm), and the proposed method can be implemented with a normal white light stereo camera and does not require highly accurate localization of the PM. 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.

Self-supervised dual-head attentional bootstrap learning network for prostate cancer screening in transrectal ultrasound images

Current convolutional neural network-based ultrasound automatic classification models for prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning (SSL) have shown promise in addressing this problem, those data that from medical scenarios contains intra-class similarity conflicts, so using loss calculations directly that include positive and negative sample pairs can mislead training. SSL method tends to focus on global consistency at the image level and does not consider the internal informative relationships of the feature map. To improve the efficiency of prostate cancer diagnosis, using SSL method to learn key diagnostic information in ultrasound images, we proposed a self-supervised dual-head attentional bootstrap learning network (SDABL), including Online-Net and Target-Net. Self-Position Attention Module (SPAM) and adaptive maximum channel attention module (CAAM) are inserted in both paths simultaneously. They captures position and inter-channel attention and of the original feature map with a small number of parameters, solve the information optimization problem of feature maps in SSL. In loss calculations, we discard the construction of negative sample pairs, and instead guide the network to learn the consistency of the location space and channel space by drawing closer to the embedding representation of positive samples continuously. We conducted numerous experiments on the prostate Transrectal ultrasound (TRUS) dataset, experiments show that our SDABL pre-training method has significant advantages over both mainstream contrast learning methods and other attention-based methods. Specifically, the SDABL pre-trained backbone achieves 80.46% accuracy on our TRUS dataset after fine-tuning.

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.

Impact of Relative Volume Difference Between Magnetic Resonance Imaging and Three-dimensional Transrectal Ultrasound Segmentation on Clinically Significant Prostate Cancer Detection in Fusion Magnetic Resonance Imaging–targeted Biopsy

BackgroundSegmentation of three-dimensional (3D) transrectal ultrasound (TRUS) images is known to be challenging, and the clinician often lacks a reliable and easy-to-use indicator to assess its accuracy during the fusion magnetic resonance imaging (MRI)-targeted prostate biopsy procedure. ObjectiveTo assess the effect of the relative volume difference between 3D-TRUS and MRI segmentation on the outcome of a targeted biopsy. Design, setting, and participantsAll adult males who underwent an MRI-targeted prostate biopsy for clinically suspected prostate cancer between February 2012 and July 2021 were consecutively included. InterventionAll patients underwent a fusion MRI-targeted prostate biopsy with a Koelis device. Outcome measurements and statistical analysisThree-dimensional TRUS and MRI prostate volumes were calculated using 3D prostate models issued from the segmentations. The primary outcome was the relative segmentation volume difference (SVD) between transrectal ultrasound and MRI divided by the MRI volume (SVD = MRI volume − TRUS volume/MRI volume) and its correlation with clinically significant prostate cancer (eg, International Society of Urological Pathology [ISUP] ≥2) positiveness on targeted biopsy cores. Results and limitationsOverall, 1721 patients underwent a targeted biopsy resulting in a total of 5593 targeted cores. The median relative SVD was significantly lower in patients diagnosed with clinically significant prostate cancer than in those with ISUP 0–1: (6.7% [interquartile range {IQR} –2.7, 13.6] vs 8.0% [IQR 3.3, 16.4], p < 0.01). A multivariate regression analysis showed that a relative SVD of >10% of the MRI volume was associated with a lower detection rate of clinically significant prostate cancer (odds ratio = 0.74 [95% confidence interval: 0.55–0.98]; p = 0.038). ConclusionsA relative SVD of >10% of the MRI segmented volume was associated with a lower detection rate of clinically significant prostate cancer on targeted biopsy cores. The relative SVD can be used as a per-procedure quality indicator of 3D-TRUS segmentation. Patient summaryA discrepancy of ≥10% between segmented magnetic resonance imaging and transrectal ultrasound volume is associated with a reduced ability to detect significant prostate cancer on targeted biopsy cores.

Posterior surgical approach for excision of retrorectal epidermal inclusion cyst, a road less travelled by surgeon: case report

Retrorectal epidermal inclusion cysts are rare congenital cystic lesions, mostly an incidental finding usually diagnosed in middle-aged women. Surgical resection is the main stay of treatment and 3 operative approaches are commonly used: abdominal, trans-sacral/coccygeal (posterior), and combined abdomino-posterior depending on the nature and location of the lesion. We report a case of a 22-year-old male presented with complaints of on-off constipation since 4 years, perineal heaviness since 2 years. Based on digital rectal exam, trans-rectal ultrasound, computed tomography, and magnetic resonance imaging findings, a presumptive diagnosis of retrorectal cyst was made. Patient underwent excision of cystic lesion by posterior trans-coccygeal approach. Post operative histopathology confirmed diagnosis of the retrorectal epidermal inclusion cyst. On follow-up after one month, symptoms of constipation and perineal heaviness completely resolved. Posterior resection allows proximal extension for elimination of the infection and in cases of adherence of the cyst to surrounding structures or in malignancy, which require en bloc resection. Posterior approach for resection is preferred for patients with posterior retrorectal cyst or presacral cyst because it provides adequate exposure with minimum dissection.

A Prospective Comparison of Transrectal Standard, Cognitive, Transperineal Fusion, and Mapping Prostate Biopsy for Cancer Detection.

Purpose: The aim of this research was to compare the clinically significant prostate cancer (csPCa) detection rate (International Society of Urological Pathology [ISUP] ≥2) for the four biopsy methods: transrectal ultrasound-guided biopsy (TRUS-GB), cognitive transrectal biopsy (COG-TB), fusion transperineal biopsy (FUS-TB), and transperineal template mapping biopsy (TPMB). Materials and Methods: The inclusion criteria were as follows: prostate-specific antigen (PSA) >2 ng/mL, and/or positive digital rectal examination (DRE), and/or suspicious lesion on transrectal ultrasound (TRUS) and Prostate Imaging Reporting and Data System (Pi-RADS) v2.1 ≥ 3 score. In total, 102 patients were enrolled in the study. Biopsies were performed by two urologists. In a single procedure, the first urologist performed a FUS-TB and TPMB followed by second urologist who performed TRUS-GB and COG-TB. All specimens were obtained within a single procedure. Results: The csPCa detection rate and overall cancer detection rate (CDR) per patient were comparable among the respective biopsy methods (p > 0.05). Compared with other biopsy methods, a lower clinically insignificant prostate cancer (cisPCa) was detected using COG-TB (p = 0.004). The positive cores percentage ratio (p < 0.001) as well as positive cores containing csPCa percentage ratio (p < 0.001) significantly increased for the targeted biopsy methods. The median maximum cancer core length (MCCL; p = 0.52) as well the median for the MCCL of csPCa (p = 0.47) did not differ significantly among the respective biopsy methods. Concordance of the Gleason scores between biopsy and postprostatectomy pathology did not differ significantly among biopsy methods (p = 0.87). For TRUS-GB, FUS-TB, and TPMB, the common predictive factors for csPCa were positive DRE, suspicious lesion on ultrasound and Pi-RADS 5. As for COG-TB, the only predictor was Pi-RADS 5. Conclusion: The targeted methods did not show an increase in detection of csPCa and overall CDR over systematic ones in patients with Pi-RADS ≥3. A lower cisPCa was detected using COG-TB in comparison with the other methods. The sampling efficiency increased for the targeted biopsy methods, which used only a proportion of positive cores and cores containing csPCa. There was no statistical difference in histology concordance among the biopsies. One common predictive factor of increased csPCa detection for all biopsy methods was Pi-RADS 5.