Prostate Region Research Articles

Clinical-radiomics combination model for predicting the short-term efficacy of bipolar transurethral enucleation of the prostate in patients with benign prostatic hyperplasia

Abstract Background Bipolar transurethral enucleation of the prostate (B-TUEP) is a well-established surgical treatment for benign prostatic hyperplasia (BPH); however, its efficacy may vary depending on patient characteristics. Magnetic resonance imaging with radiomics analysis can offer comprehensive and quantitative information about prostate characteristics that may relate to surgical outcomes. This study aimed to explore the value of magnetic resonance imaging and radiomics analysis in predicting the short-term efficacy of B-TUEP for BPH. Materials and methods A total of 137 patients with BPH who underwent B-TUEP at 2 institutions were included. Radiological features were measured in the magnetic resonance images, and the radiomics score was developed from 1702 radiomics features extracted from the prostate and transitional zone regions of interest. Three prediction models were developed and validated based on clinical-radiological features, radiomic features, and their combinations. The models were evaluated using the area under the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results The combination model exhibited the highest area under curve in both the training set (0.838) and the external validation set (0.802), indicating superior predictive performance and robustness. Furthermore, the combination model demonstrated good calibration (p > 0.05) and optimal clinical utility. The combination model indicated that a higher maximum urine flow rate, lower transitional zone index, and higher radiomics score were associated with an increased risk of poor efficacy. Conclusions Magnetic resonance imaging with radiomic analysis can offer valuable insights for predicting the short-term efficacy of B-TUEP in patients with BPH. A combination model based on clinical and radiomics features can assist urologists in making more precise clinical decisions.

A mixed Mamba U-net for prostate segmentation in MR images

The diagnosis of early prostate cancer depends on the accurate segmentation of prostate regions in magnetic resonance imaging (MRI). However, this segmentation task is challenging due to the particularities of prostate MR images themselves and the limitations of existing methods. To address these issues, we propose a U-shaped encoder-decoder network MM-UNet based on Mamba and CNN for prostate segmentation in MR images. Specifically, we first proposed an adaptive feature fusion module based on channel attention guidance to achieve effective fusion between adjacent hierarchical features and suppress the interference of background noise. Secondly, we propose a global context-aware module based on Mamba, which has strong long-range modeling capabilities and linear complexity, to capture global context information in images. Finally, we propose a multi-scale anisotropic convolution module based on the principle of parallel multi-scale anisotropic convolution blocks and 3D convolution decomposition. Experimental results on two public prostate MR image segmentation datasets demonstrate that the proposed method outperforms competing models in terms of prostate segmentation performance and achieves state-of-the-art performance. In future work, we intend to enhance the model's robustness and extend its applicability to additional medical image segmentation tasks.

A Comparative Analysis of U-Net and Vision Transformer Architectures in Semi-Supervised Prostate Zonal Segmentation.

The precise segmentation of different regions of the prostate is crucial in the diagnosis and treatment of prostate-related diseases. However, the scarcity of labeled prostate data poses a challenge for the accurate segmentation of its different regions. We perform the segmentation of different regions of the prostate using U-Net- and Vision Transformer (ViT)-based architectures. We use five semi-supervised learning methods, including entropy minimization, cross pseudo-supervision, mean teacher, uncertainty-aware mean teacher (UAMT), and interpolation consistency training (ICT) to compare the results with the state-of-the-art prostate semi-supervised segmentation network uncertainty-aware temporal self-learning (UATS). The UAMT method improves the prostate segmentation accuracy and provides stable prostate region segmentation results. ICT plays a more stable role in the prostate region segmentation results, which provides strong support for the medical image segmentation task, and demonstrates the robustness of U-Net for medical image segmentation. UATS is still more applicable to the U-Net backbone and has a very significant effect on a positive prediction rate. However, the performance of ViT in combination with semi-supervision still requires further optimization. This comparative analysis applies various semi-supervised learning methods to prostate zonal segmentation. It guides future prostate segmentation developments and offers insights into utilizing limited labeled data in medical imaging.

Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy

Purpose objective(s)Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques.Materials/methodsWe used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT.ResultsThe average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques.ConclusionResults demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning.

Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer.

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7ms [95% CI: 146.9-156.5ms] compared to 80.9ms for malignant lesions [95% CI: 67.9-79.1ms]; p < 0.001). Based on this assessment, a limit of 109.2ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate.

Accelerated mapping and robust parallel transmit pulse design for heart and prostate imaging at 7 T.

To investigate the impact of reduced k-space sampling on mapping and the resulting impact on phase shimming and dynamic/universal parallel transmit (pTx) RF pulse design. Channel-wise 3D maps were measured at 7 T in 35 and 23 healthy subjects for the heart and prostate region, respectively. With these maps, universal phase shims optimizing homogeneity and efficiency were designed for heart and prostate imaging. In addition, universal 4kT-point pulses were designed for the heart. Subsequently, individual phase shims and individual 4kT-pulses were designed based on maps with different acceleration factors and tested on the original maps. The performance of the pulses was compared by evaluating their coefficients of variation (CoV), efficiencies and specific energy doses (SED). Furthermore, validation measurements were carried out for one heart and one prostate subject. For both organs, the universal phase shims showed significantly higher efficiencies and lower CoVs compared to the vendor provided default shim, but could still be improved with individual phase shims based on accelerated maps (acquisition time = 30 s). In the heart, the universal 4kT-pulse achieved significantly lower CoVs than tailored phase shims. Tailored 4kT-pulses based on accelerated maps resulted in even further reduced CoVs or a 2.5-fold reduction in SED at the same CoVs as the universal 4kT-pulse. Accelerated maps can be used for the design of tailored pTx pulses for prostate and cardiac imaging at 7 T, which further improve homogeneity, efficiency, or SED compared to universal pulses.

A sperm-activating trypsin-like protease from the male reproductive tract of Spodoptera litura: Proteomic identification, sequence characterization, gene expression profile, RNAi and the effects of ionizing radiation

Like other lepidopteran insects, males of the tobacco cutworm moth, Spodoptera litura produce two kinds of spermatozoa, eupyrene (nucleate) and apyrene (anucleate) sperm. Formed in the testis, both kinds of sperm are released into the male reproductive tract in an immature form and are stored in the duplex region of the tract. Neither type of sperm is motile at this stage. When stored apyrene sperm from the duplex are treated in vitro with an extract of the prostatic region of the male tract, or with mammalian trypsin, they become motile; activation is greater and achieved more rapidly with increasing concentration of extract or enzyme. The activating effect of prostatic extract is blocked by soybean trypsin inhibitor (SBTI), also in a dose-dependent way. These results suggest that the normal sperm-activating process is due to an endogenous trypsin-like protease produced in the prostatic region.Proteomic analysis of S. litura prostatic extracts revealed a Trypsin-Like Serine Protease, TLSP, molecular weight 27 kDa, whose 199-residue amino acid sequence is identical to that of a predicted protein from the S. litura genome and is highly similar to predicted proteins encoded by genes in the genomes of several other noctuid moth species. Surprisingly, TLSP is only distantly related to Serine Protease 2 (initiatorin) of the silkmoth, Bombyx mori, the only identified lepidopteran protein so far shown to activate sperm. TLSP has features typical of secreted proteins, probably being synthesized as an inactive precursor zymogen, which is later activated by proteolytic cleavage.cDNA was synthesized from total RNA extracted from the prostatic region and was used to examine TLSP expression using qPCR. tlsp mRNA was expressed in both the prostatic region and the accessory glands of the male tract. Injection of TLSP-specific dsRNA into adult males caused a significant reduction after 24 h in tlsp mRNA levels in both locations. The number of eggs laid by females mated to adult males that were given TLSP dsRNA in 10 % honey solution, and the fertility (% hatched) of the eggs were reduced. Injecting pupae with TLSP dsRNA caused the later activation of apyrene sperm motility by adult male prostatic extracts to be significantly reduced compared to controls.Exposure of S. litura pupae to ionizing radiation significantly reduced expression of tlsp mRNA in the prostatic part and accessory gland of irradiated males in both the irradiated generation and also in their (unirradiated) F1 progeny. The implications of these findings for the use of the inherited sterility technique for the control of S. litura and other pest Lepidoptera are discussed.

A Prostate Boundary Localization and Edge Denoising Algorithm

This research aimed at presenting a two-step method for prostate segmentation in TRUS images. The research used a prostate boundary localization and prostate edge denoising approach. The proposed method contribution is the use of the optimized Hodge’s method as the boundary operator and the use of the Bidirectional Exponential moving average to perform edge denoising. The results showed that the proposed method is effective in completing the prostate segmentation task. (1) The prostate region is effectively initialized and localized. (2) The recovery of noise points is accomplished and the segmentation result being consistent with the general shape of the prostate. The experimental results showed that this method can improve the overall segmentation accuracy. The process uses a combination of traditional and unsupervised methods, eliminating the need to rely on large data sets compared to current deep learning methods. The proposed method achieved excellent segmentation accuracy, with the Dice similarity coefficient (DICE) value of 0.9679, an average Intersection over Union (IoU) value of 0.9377, and an average False Positive Rate (FPR) of 0.0399. The results obtained from this study have significant implications for clinical practice. Accurate prostate segmentation is crucial for various applications, including radiation therapy planning, image-guided interventions, and computer-aided diagnosis. The proposed method has the potential to improve these applications by providing more precise and reliable prostate segmentations. However, it is important to acknowledge some limitations of this study. First, the proposed method was evaluated on a limited dataset, which may not fully represent the diversity of prostate images encountered in clinical practice. Further validation on larger datasets is necessary to assess its generalizability. Additionally, the proposed method relied on manual annotations for training, which can introduce inter-observer variability. Incorporating automated or semi-automated annotation techniques could enhance the robustness of the method

Fast and accessible T2 mapping using off-resonance corrected DESPOT2 with application to 3D prostate

PurposeMost T1 and T2 mapping take long acquisitions or needs specialized sequences not widely accessible on clinical scanners. An available solution is DESPOT1/T2 (Driven equilibrium single pulse observation of T1/T2). DESPOT1/T2 uses Spoiled gradient-echo (SPGR) and balanced Steady-State Free Precession (bSSFP) sequences, offering an accessible and reliable way for 3D accelerated T1/T2 mapping. However, bSSFP is prone to off-resonance artifacts, limiting the application of DESPOT2 in regions with high susceptibility contrasts, like the prostate. Our proposal, DESPO+, employs the full bSSFP and SPGR models with a dictionary-based method to reconstruct 3D T1/T2 maps in the prostate region without off-resonance banding. MethodsDESPO+ modifies the bSSFP acquisition of the original variable flip angle DESPOT2. DESPO+ uses variable repetition and echo times, employing a dictionary-based method of the full bSSFP and SPGR models to reconstruct T1, T2, and Proton Density (PD) simultaneously. The proposed DESPO+ method underwent testing through simulations, T1/T2 phantoms, and on fourteen healthy subjects. ResultsThe results reveal a significant reduction in T2 map banding artifacts compared to the original DESPOT2 method. DESPO+ approach reduced T2 errors by up to seven times compared to DESPOT2 in simulations and phantom experiments. We also synthesized in-vivo T1-weighted/T2-weighted images from the acquired maps using a spin-echo model to verify the map's quality when lacking a reference. For in-vivo imaging, the synthesized images closely resemble those from the clinical MRI protocol, reducing scan time by around 50% compared to traditional spin-echo T1-weighted/T2-weighted acquisitions. ConclusionDESPO+ provides an off-resonance insensitive and clinically available solution, enabling high-resolution 3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire prostate, all achieved within a short scan time of 3.6 min, similar to DESPOT1/T2.

Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.

To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more reliable evaluation of its fitness in prostate regions that were not identified as suspicious for cancer by a human-reader in pre- and intra-biopsy imaging analysis. Low-level pre-biopsy MRI biomarkers from targeted and non-targeted biopsy locations were extracted and statistically tested for representativeness against biomarkers from non-biopsied prostate regions. A probabilistic machine learning classifier was optimized to map biomarkers to their core-level pathology, followed by extrapolation of pathology scores to non-biopsied prostate regions. Goodness-of-fit was assessed at targeted and non-targeted biopsy locations for the post-biopsy individual patient. Our experiments showed high predictability of imaging biomarkers in differentiating histopathology scores in thousands of non-targeted core-biopsy locations (ROC-AUCs: 0.85-0.88), but also high variability between patients (Median ROC-AUC [IQR]: 0.81-0.89 [0.29-0.40]). The sparseness of prostate biopsy data makes the validation of a whole gland risk mapping a non-trivial task. Previous studies i) focused on targeted-biopsy locations although biopsy-specimens drawn from systematically scattered locations across the prostate constitute a more representative sample to non-biopsied regions, and ii) estimated prediction-power across predicted instances (e.g., biopsy specimens) with no patient distinction, which may lead to unreliable estimation of model fitness to the individual patient due to variation between patients in instance count, imaging characteristics, and pathologies. This study proposes a personalized whole-gland prostate cancer risk mapping post-biopsy to allow clinicians to better stage and personalize focal therapy treatment plans.

Contribution of 68 Ga-DOTA-FAPI-04 PET/CT to Prostate Cancer Imaging : Complementary Role in PSMA-Negative Cases.

Prostate-specific membrane antigen (PSMA)-targeted PET/CT is a well-established imaging method in prostate cancer (PC) for both staging and restaging, and also for theranostic applications. An alternative imaging method is crucial for 15% PSMA-negative cases. We aimed to investigate the contribution of 68 Ga-DOTA-FAPI-04 PET/CT to PC imaging. Thirty-six patients diagnosed with PC were included. Patients underwent both 68 Ga-PSMA PET/CT and 68 Ga-DOTA-FAPI-04 PET/CT imaging within 1 week. In staging group, primary tumor uptake values were compared, and also correlations were done with histopathological findings, MRI findings, and total PSA levels. In biochemical recurrence group, the uptake values in prostatic region and metastases were evaluated to define the local recurrence or metastatic disease. In staging group, PSMA PET showed increased uptake in the primary lesion area in 14/27 (52%) patients, whereas 20/27 (74%) patients were positive in FAPI-04 PET. FAPI-04 positivity was found to be quite high, such as 54%, in PSMA-negative patients. A significant difference was observed between ISUP grade 1-3 patients and ISUP grade 4-5 patients in FAPI-04 PET ( P = 0.03). Local recurrence was detected in 3 patients, pelvic lymph node metastasis in 1 patient, and sacrum metastasis in 1 patient in biochemical recurrence group, and all of the lesions had more intense uptake in PSMA PET than FAPI-04 PET. FAPI PET imaging seems to have a potential to contribute PSMA PET imaging with FAPI positivity in more than half of PSMA-negative cases. Also, FAPI-targeted radionuclide therapy may be a promising method in patients resistant to PSMA-targeted therapy.

Distribution of neurovascular structures within the prostate gland and their relationship to complications after radical prostatectomy.

Radical prostatectomy remains the main choice of treatment for prostate cancer. However, despite improvements in surgical techniques and neurovascular sparing procedures, rates of erectile dysfunction, and urinary incontinence remain variable. This is due, at least in part, to an incomplete understanding of neurovascular structures associated with the prostate. The objective of this study was to provide a comprehensive, detailed histological overview of the distribution of nerves and blood vessels within the prostate, facilitating subsequent correlation of prostatic neurovascular structures with patients' clinical outcomes after radical prostatectomy. Neurovascular structures within the prostate were investigated in a total of 309 slides obtained from 15 patients who underwent non-nerve-sparing radical prostatectomy. Immunohistochemical staining was performed to identify and distinguish between parasympathetic and sympathetic nerves, whereas hematoxylin and eosin staining was used to identify blood vessels. The total number, density, and relative position of nerves and blood vessels were established using quantitative morphometry and illustrated using visualization approaches. Patient-specific outcome data were then used to establish whether the internal distribution of nerves and blood vessels within the prostate correlated with the nature and extent of complications after surgery. One-way analysis of variance tests and unpaired ttests were applied to establish statistically significant differences across the measured variables. Nerves and blood vessels were present across all prostatic levels and regions. However, their number and density varied considerably between regions. Assessment of the precise positioning of neurovascular structures revealed that the majority of nerve fibers were located within the dorsal and peripheral aspects of the gland. In contrast, blood vessels were predominantly located within ventral and dorsal midline regions. The number of intraprostatic nerves was found to be significantly lower in patients who recovered their continence within 12 months of surgery, compared to those whose recovery took 12 months or longer. We report an unexpected disconnect between the localization and positioning of nerve fibers and blood vessels within the prostate. Moreover, individual variability in the density of intraprostatic neurovascular structures appears to correlate with the successful recovery of urinary continence after radical prostatectomy, suggesting that differences in intrinsic neurovascular arrangements of the prostate influence postoperative outcomes.

Longitudinal analysis of T2 relaxation time variations following radiotherapy for prostate cancer

Aim of this paper is to evaluate short and long-term changes in T2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T2 relaxation times were calculated for quantitative analysis. The T2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T2 values between the baseline and subsequent measurements, particularly between pre-therapy (M0) and two weeks post-therapy (M1), as well as during the monthly interval checks (M2 - M6). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.

Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate.

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov-Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences.

Exploring multi-b-value diffusion-weighted imaging for more accurate prostate segmentation and uncertainty estimation

Background and objectiveAccurate auto-segmentation on diffusion-weighted imaging (DWI) remains a challenge. A 3D convolutional neural network (CNN) model using DWI was developed to automatically segment the prostate gland and estimate uncertainty in precision treatment planning. MethodsA total of 303 patients who uncervent multi-b-value DWI were retrospectively recruited and divided into training and testing groups based on the examination time. Radiologists labeled the prostate region on DWI images of b = 800 sec/mm2. A 3D U-shaped CNN was trained on multi-b-value DWI images (multi-b-value model) and evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). Segmentation results were compared to a single-b-value model and registration result from T2-weighted images. The study estimated and analyzed the combination of epistemic and aleatoric uncertainty. ResultsThe DSC, HD, and ASD of our final prediction were 0.928 ± 0.036, 5.472 ± 4.008 mm, and 0.575 ± 0.508 mm. The proposed multi-b-value model achieved a higher DSC on DWI images with all b-values when comparing with the single-b-value model, and superior to the registration result on most of the b-values. The uncertainty helps to effectively filter out prediction errors and indicates that the model is more confident in the middle of the prostate gland, followed by the base and apex of the prostate gland. ConclusionsThe complementary information provided by the multi-b-value DWI images helps to improve both the performance of prostate segmentation, while also providing an estimation of the uncertainty associated with the segmentation prediction, which is clinically useful.

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.

A novel Residual and Gated Network for prostate segmentation on MR images

Reliable and automated segmentation of the prostate in magnetic resonance (MR) images is critical for the diagnosis and treatment of prostate cancer. The unclear boundary of the prostate increases the difficulty of correctly differentiating the prostate region from the surrounding regions. This paper proposes a novel approach with Residual and Gated Network (ResGNet) for prostate segmentation on MR images. The ResGNet block fuses the residual and gated connections in a multi-directional and multi-path learning strategy for enhancing information propagation and preserving important boundary information. The proposed method is conducted on several public databases, and an average DSC of 94.4%, 95-HD of 3.280 mm, and ABD of 0.919 mm are obtained in the 5-fold cross-validation experiment on the PROMISE12 dataset. Experimental results demonstrate that the ResGNet outperforms the state-of-the-art methods and is effective and robust regardless of the variability of MR scanners and acquisition protocols or the size and shape of the prostate gland for the segmentation of healthy or diseased prostates.

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.

CCT-Unet: A U-Shaped Network Based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI.

The accurate segmentation of prostate region in magnetic resonance imaging (MRI) can provide reliable basis for artificially intelligent diagnosis of prostate cancer. Transformer-based models have been increasingly used in image analysis due to their ability to acquire long-term global contextual features. Although Transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate MRI due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (CNN) could retain these local features well. Therefore, a robust prostate segmentation model that can aggregate the characteristics of CNN and Transformer is desired. In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection. Extensive experiments have been conducted to compare our CCT-Unet with several state-of-the-art methods on both the ProstateX open dataset and the self-bulit Huashan dataset, and the results have consistently shown the accuracy and robustness of our CCT-Unet in MRI prostate segmentation.

Biocompatibility and efficacy of prostatic urethral lift in benign prostate hyperplasia: an in vivo and in vitro study

The study aimed to assess the biocompatibility and efficacy of a prostatic urethral lift (PUL) for benign prostatic hyperplasia (BPH). Human BPH-1 cells were co-cultured with implant anchors and sutures, and cytotoxicity was measured. Scanning electron microscopy (SEM) was used to observe adhesion and growth of cells and to evaluate implant biocompatibility. Fifteen male beagle dogs were randomly assigned to the surgical (n = 9) or sham-operated (n = 6) groups. The surgical group underwent cystotomy, and PUL was used to insert two implants in each lobe of the prostate to compress the enlarged prostate and dilate the urethra; the sham group underwent cystotomy without implant insertion. Compared with the control group, no significant difference in cell viability among the groups with different co-culture times of implant anchors and sutures (P > 0.05) was observed. SEM revealed good adhesion and growth of prostate cells on the implants. Improvements in urine flow rates remained stable at 7, 28, and 180 days after surgery, and the urethral diameter in the prostate region was significantly increased compared with that before surgery. PUL is a biocompatible and effective treatment for BPH, improving the urine flow rate without causing inflammation, tissue damage, or cytotoxic effects. Here, the basis for further PUL application was provided.