A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging

ObjectiveTo develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions.MethodsOf all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric.ResultsThe proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results.ConclusionThe proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis

Prostate Segmentation on Magnetic Resonance Imaging

Automatic and precise segmentation of the prostate is beneficial to various diagnostic and therapeutic procedures on magnetic resonance imaging. However, the work is very challenging because of the heterogeneity of prostate tissue, the lack of clearly defined boundaries, and the wide variation in prostate shape between individuals. Based on the segmentation scheme for the prostate and its lesion regions, a new deep convolution neural network is proposed in this research. To acquire excellent segmentation performance with consistency in both appearance and space, CRF-RNN is added on top of the network. By introducing an attention mechanism, the network is made to focus more feature on the prostate zones in both channel and spatial dimensions. In addition, a new dense block is created to stabilize parameter updates and prevent gradients from disappearing as the network deepens. Finally, the model was trained and validated using the real prostate dataset of 180 patients with four cross-validations. The proposed model achieves 95% HD, 86.82%, 93.90%, 94.11%, 93.8%, 7.84% for prostate, 79.2%, 89.51%, 88.43%, 89.31%, 8.39% for lesion area in segmentation in terms of IOU, Dice score, accuracy, and sensitivity. Compared to the state-of-the-art models FCN, U-Net, U-Net++ and ResU-Net, the segmentation model shows more promising results. With an outstanding achievement in automated segmentation of prostate and lesion regions, the presented model highlights the ability of the novel deep convolutional neural network to facilitate clinical disease intervention and management

Investigation of the voltage collapse mechanism in three-phase PWM rectifiers

Three-phase pulse width modulation (PWM) rectifiers are usually designed under the assumption of ideal ac power supply and input inductance. However, non-ideal circuit parameters may lead to a voltage collapse of PWM rectifiers. This paper investigates the mechanism of voltage collapse in three-phase PWM rectifiers. An analytical stability boundary expression is derived by analyzing the equilibrium point of the averaging state space model, which can not only accurately locate the voltage collapse boundary in the circuit parameter domain, but also reveal the essential characteristic of the voltage collapse. Results are obtained and compared with those of the trial-error method and the Jacobian method. Based on the analysis results, the system parameters can be divided into two categories. One of these categories affects the critical point, and other affects only the instability process. Furthermore, an effective control strategy is proposed to prevent a vulnerable system from being driven into the instability region. The analysis results are verified by the experiments.Published versio

Prostate Segmentation in MRI Using Transformer Encoder and Decoder Framework

To develop an accurate segmentation model for the prostate and lesion area to help clinicians diagnose diseases, we propose a multi-encoder and decoder segmentation network, denoted Muled-Net, which can concurrently segment the prostate and lesion regions in an image. The model performs parallel calculations for dual input. In two encoder branches of the model, a new transformer encoder is used to overcome the fact that only information from the neighborhood pixels can be captured, increasing the ability to capture global dependencies. Furthermore, given the usually small size of the lesion, ASPP and feature fusion are merged to expand the perceptual field and retain more contextual information of the shallow layer in decoder. To the best of our limited knowledge, there is no public dataset for the segmentation of the prostate and its lesion regions. So we made a publicly usable dataset. Muled-Net is compared with other deep learning methods, FCN, U-Net, U-Net++, and ResU-Net with four-fold cross-validation. Of all 218 subjects, 140 healthy individuals and 78 patients with prostate cancer were included in this work. Average Dice of 95%, Iou of 89%, sensitivity of 94%, 95HD of 9.56, and MSD of 0.66 are achieved for the prostate segmentation and average Dice of 89%, Iou of 82%, sensitivity of 92%, 95HD of 11.16, and MSD of 1.09 for the segmentation of the prostate lesion regions. The performance of the proposed model has made significant improvements to the segmentation of the lesion regions in particular, suggesting that the model could be considered as an auxiliary tool to ease the workload of physicians and help them in making treatment decisions

Production of <it>N</it>^<it>α</it>-acetylated thymosin α1 in <it>Escherichia coli</it>

Abstract Background Thymosin α1 (Tα1), a 28-amino acid Nα-acetylated peptide, has a powerful general immunostimulating activity. Although biosynthesis is an attractive means of large-scale manufacture, to date, Tα1 can only be chemosynthesized because of two obstacles to its biosynthesis: the difficulties in expressing small peptides and obtaining Nα-acetylation. In this study, we describe a novel production process for Nα-acetylated Tα1 in Escherichia coli. Results To obtain recombinant Nα-acetylated Tα1 efficiently, a fusion protein, Tα1-Intein, was constructed, in which Tα1 was fused to the N-terminus of the smallest mini-intein, Spl DnaX (136 amino acids long, from Spirulina platensis), and a His tag was added at the C-terminus. Because Tα1 was placed at the N-terminus of the Tα1-Intein fusion protein, Tα1 could be fully acetylated when the Tα1-Intein fusion protein was co-expressed with RimJ (a known prokaryotic Nα-acetyltransferase) in Escherichia coli. After purification by Ni-Sepharose affinity chromatography, the Tα1-Intein fusion protein was induced by the thiols β-mercaptoethanol or d,l-dithiothreitol, or by increasing the temperature, to release Tα1 through intein-mediated N-terminal cleavage. Under the optimal conditions, more than 90% of the Tα1-Intein fusion protein was thiolyzed, and 24.5 mg Tα1 was obtained from 1 L of culture media. The purity was 98% after a series of chromatographic purification steps. The molecular weight of recombinant Tα1 was determined to be 3107.44 Da by mass spectrometry, which was nearly identical to that of the synthetic version (3107.42 Da). The whole sequence of recombinant Tα1 was identified by tandem mass spectrometry and its N-terminal serine residue was shown to be acetylated. Conclusions The present data demonstrate that Nα-acetylated Tα1 can be efficiently produced in recombinant E. coli. This bioprocess could be used as an alternative to chemosynthesis for the production of Tα1. The described methodologies may also be helpful for the biosynthesis of similar peptides.</p