Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.

Background and Objective: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical intervention planning, and follow-up assessments. Kidney US image segmentation consists of extracting useful objects or regions from the total image, which helps determine tissue organization and improve diagnosis. Thus, obtaining accurate kidney segmentation data is an important first step for precisely diagnosing kidney diseases. However, manual delineation of the kidney in US images is complex and tedious in clinical practice. To overcome these challenges, we developed a novel automatic method for US kidney segmentation. Methods: Our method comprises two cascaded steps for US kidney segmentation. The first step utilizes a coarse segmentation procedure based on a deep fusion learning network to roughly segment each input US kidney image. The second step utilizes a refinement procedure to fine-tune the result of the first step by combining an automatic searching polygon tracking method with a machine learning network. In the machine learning network, a suitable and explainable mathematical formula for kidney contours is denoted by basic parameters. Results: Our method is assessed using 1380 trans-abdominal US kidney images obtained from 115 patients. Based on comprehensive comparisons of different noise levels, our method achieves accurate and robust results for kidney segmentation. We use ablation experiments to assess the significance of each component of the method. Compared with state-of-the-art methods, the evaluation metrics of our method are significantly higher. The Dice similarity coefficient (DSC) of our method is 94.6 ± 3.4%, which is higher than those of recent deep learning and hybrid algorithms (89.4 ± 7.1% and 93.7 ± 3.8%, respectively). Conclusions: We develop a coarse-to-refined architecture for the accurate segmentation of US kidney images. It is important to precisely extract kidney contour features because segmentation errors can cause under-dosing of the target or over-dosing of neighboring normal tissues during US-guided brachytherapy. Hence, our method can be used to increase the rigor of kidney US segmentation.

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

Magnetic Resonance Imaging-Based Radiomics Nomogram to Predict Intraoperative Hemorrhage of Placenta Previa.

OBJECTIVE: Placenta previa (PP) is associated with intraoperative and postpartum hemorrhage, increased maternal morbidity and mortality. We aimed to develop a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for PP. STUDY DESIGN: A total of 125 PP pregnant women were divided into a training set (n = 80) and a validation set (n = 45). An MRI-based model was built for the classification of patients into IPH and non-IPH groups in a training set and a validation set. Multivariate nomograms were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. RESULTS: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus, and placental signals in the cervix were significantly independent predictors for IPH (all ps < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. Area under the ROC curve was 0.918 (95% confidence interval [CI]: 0.857-0.979) in the training set and 0.866 (95% CI: 0.748-0.985) in the validation set by the combination of four MRI features. CONCLUSION: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for PP. Our study enables obstetricians to perform adequate preoperative evaluation to reduce blood loss and cesarean hysterectomy. KEY POINTS: · MRI is an important method for preoperative assessment of the risk of placenta previa.. · MRI-based nomogram can assess the risk of intraoperative bleeding of placenta previa.. · MRI is helpful for more comprehensive evaluation of placenta previa and adequate preoperative preparation..

A Robust and Explainable Structure-Based Algorithm for Detecting the Organ Boundary From Ultrasound Multi-Datasets.

Detecting the organ boundary in an ultrasound image is challenging because of the poor contrast of ultrasound images and the existence of imaging artifacts. In this study, we developed a coarse-to-refinement architecture for multi-organ ultrasound segmentation. First, we integrated the principal curve-based projection stage into an improved neutrosophic mean shift-based algorithm to acquire the data sequence, for which we utilized a limited amount of prior seed point information as the approximate initialization. Second, a distribution-based evolution technique was designed to aid in the identification of a suitable learning network. Then, utilizing the data sequence as the input of the learning network, we achieved the optimal learning network after learning network training. Finally, a scaled exponential linear unit-based interpretable mathematical model of the organ boundary was expressed via the parameters of a fraction-based learning network. The experimental outcomes indicated that our algorithm 1) achieved more satisfactory segmentation outcomes than state-of-the-art algorithms, with a Dice score coefficient value of 96.68 ± 2.2%, a Jaccard index value of 95.65 ± 2.16%, and an accuracy of 96.54 ± 1.82% and 2) discovered missing or blurry areas.

Genetic analysis and intracytoplasmic sperm injection outcomes of Chinese patients with congenital bilateral absence of vas deferens.

PURPOSE: Congenital bilateral absence of the vas deferens (CBAVD) is a major cause of obstructive azoospermia and male factor infertility. CBAVD is mainly caused by mutations in the genes encoding CFTR (cystic fibrosis transmembrane conductance regulator) and ADGRG2 (adhesion G protein-coupled receptor G2). This study aimed to describe CFTR and ADGRG2 variations in 46 Chinese CBAVD patients and evaluated sperm retrieval and assisted reproductive technology outcomes. METHODS: The CFTR and ADGRG2 genes were sequenced and analyzed by whole-exome sequencing (WES), and variations were identified by Sanger sequencing. Bioinformatic analysis was performed. We retrospectively reviewed the outcomes of patients undergoing sperm retrieval surgery and intracytoplasmic sperm injection (ICSI). RESULTS: In total, 35 of 46 (76.09%) patients carried at least one variation in CFTR, but no copy number variants or ADGRG2 variations were found. In addition to the IVS9-5 T allele, there were 27 CFTR variations, of which 4 variations were novel and predicted to be damaging by bioinformatics. Spermatozoa were successfully retrachieved in 46 patients, and 39 of the patients had their own offspring through ICSI. CONCLUSION: There are no obvious hotspot CFTR mutations in Chinese CBAVD patients besides the IVS9-5 T allele. Therefore, WES might be the best detection method, and genetic counseling should be different from that provided to Caucasian populations. After proper counseling, all patients can undergo sperm retrieval from their epididymis or testis, and most of them can have their own children through ICSI.

Lung contour detection in Chest X-ray images using Mask Region-based Convolutional Neural Network and Adaptive Closed Polyline Searching Method.

Detection of lung contour on chest X-ray images (CXRs) is a necessary step for computer-aid medical imaging analysis. Because of the low-intensity contrast around lung boundary and large inter-subject variance, it is challenging to detect lung from structural CXR images accurately. To tackle this problem, we design an automatic and hybrid detection network containing two stages for lung contour detection on CXRs. In the first stage, an image preprocessing stage based on a deep learning model is used to automatically extract coarse lung contours. In the second stage, a refinement step is used to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. The model is evaluated on several public datasets, and experiments demonstrate that the performance of the proposed method outperforms state-of-the-art methods.Clinical Relevance- This can help radiologists for automatic separate lung, which can decrease the workloads of the radiologists' manually delineated lung contour in CXRs.