Esterification catalyzed by an efficient solid acid synthesized from PTSA and UiO-66(Zr) for biodiesel production.

p-Toluenesulfonic acid (PTSA) is a typical homogeneous acid for biodiesel production. Due to the shortcomings of high deliquescence and non-recyclability, it is necessary to synthesize a recyclable solid acid. For the sake of this, UiO-66(Zr) is used to support PTSA through defect coordination, and four different preparation routes are compared. The obtained catalyst (UiO-G) is characterized with thermogravimetry analysis (TG), X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), pyridine FTIR spectroscopy (py-FTIR), nitrogen adsorption-desorption, and base titration. In addition, the effects of esterification parameters on conversion are investigated to obtain the optimal conditions. To further verify the high catalytic activity of UiO-G, the kinetic model of solid-liquid-liquid esterification is established, in which the kinetic parameters of activation energy, reaction order, and exponential factor are calculated. Results indicate the PTSA is successfully inserted in UiO-66(Zr) without destroying its original structure. With that, the maximum conversion of oleic acid to biodiesel of 91.3% is achieved with a molar ratio of methanol/oleic acid of 12 and a catalyst amount of 8 wt% at 70 °C for 2 h. Moreover, UiO-G could remarkably reduce the activation energy, where the activation energy is 28.61 kJ mol-1, the average reaction order is 1.51, and the pre-exponential factor is 29.11 min-1.

Application of Virtual Navigation with Multimodality Image Fusion in Foramen Ovale Cannulation.

OBJECTIVE: Idiopathic trigeminal neuralgia (ITN) can be effectively treated with radiofrequency thermocoagulation. However, this procedure requires cannulation of the foramen ovale, and conventional cannulation methods are associated with high failure rates. Multimodality imaging can improve the accuracy of cannulation because each imaging method can compensate for the drawbacks of the other. We aim to determine the feasibility and accuracy of percutaneous foramen ovale cannulation under the guidance of virtual navigation with multimodality image fusion in a self-designed anatomical model of human cadaveric heads. DESIGN: Five cadaveric head specimens were investigated in this study. Spiral computed tomography (CT) scanning clearly displayed the foramen ovale in all five specimens (10 foramina), which could not be visualized using two-dimensional ultrasound alone. The ultrasound and spiral CT images were fused, and percutaneous cannulation of the foramen ovale was performed under virtual navigation. After this, spiral CT scanning was immediately repeated to confirm the accuracy of the cannulation. RESULTS: Postprocedural spiral CT confirmed that the ultrasound and CT images had been successfully fused for all 10 foramina, which were accurately and successfully cannulated. The success rates of both image fusion and cannulation were 100%. CONCLUSIONS: Virtual navigation with multimodality image fusion can substantially facilitate foramen ovale cannulation and is worthy of clinical application.

A vessel segmentation method for multi-modality angiographic images based on multi-scale filtering and statistical models.

BACKGROUND: Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases. The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i.e., dependence of the image modality, uneven contrast media, bias field, and overlapping intensity distribution of the object and background. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms. Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images. METHODS: To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities. Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions (two Exponential distributions and one Gaussian distribution) was built to fit the histogram curve of the filtered data, where the expectation maximization (EM) algorithm was used for parameters estimation. Finally, three-dimensional (3D) Markov random field (MRF) were employed to improve the accuracy of pixel-wise classification and posterior probability estimation. To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method. To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography (MRA) data sets. RESULTS: The results of the phantoms were satisfying, e.g., the noise was greatly suppressed, the percentages of the misclassified voxels, i.e., the segmentation error ratios, were no more than 0.3%, and the Dice similarity coefficients (DSCs) were above 94%. According to the opinions of clinical vascular specialists, the vessels in various data sets were extracted with high accuracy since complete vessel trees were extracted while lesser non-vessels and background were falsely classified as vessel. In the comparison experiments, the proposed method showed its superiority in accuracy and robustness for extracting vascular structures from multi-modality angiographic images with complicated background noises. CONCLUSIONS: The experimental results demonstrated that our proposed method was available for various angiographic data. The main reason was that the constructed mixture probability model could unitarily classify vessel object from the multi-scale filtered data of various angiography images. The advantages of the proposed method lie in the following aspects: firstly, it can extract the vessels with poor angiography quality, since the multi-scale filtering algorithm can improve the vessel intensity in the circumstance such as uneven contrast media and bias field; secondly, it performed well for extracting the vessels in multi-modality angiographic images despite various signal-noises; and thirdly, it was implemented with better accuracy, and robustness than the traditional methods. Generally, these traits declare that the proposed method would have significant clinical application.

Brain MR image denoising for Rician noise using pre-smooth non-local means filter.

BACKGROUND: Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. METHODS: Considering the characteristics of both Rician noise and the NLM filter, this study proposes a frame for a pre-smoothing NLM (PSNLM) filter combined with image transformation. In the PSNLM frame, noisy MRI is first transformed into an image in which noise can be treated as additive noise. Second, the transformed MRI is pre-smoothed via a traditional denoising method. Third, the NLM filter is applied to the transformed MRI, with weights that are computed from the pre-smoothed image. Finally, inverse transformation is performed on the denoised MRI to obtain the denoising results. RESULTS: To test the performance of the proposed method, both simulated and real patient data are used, and various pre-smoothing (Gaussian, median, and anisotropic filters) and image transformation [squared magnitude of the MRI, and forward and inverse variance-stabilizing trans-formations (VST)] methods are used to reduce noise. The performance of the proposed method is evaluated through visual inspection and quantitative comparison of the peak signal-to-noise ratio of the simulated data. The real data include Alzheimer's disease patients and normal controls. For the real patient data, the performance of the proposed method is evaluated by detecting atrophy regions in the hippocampus and the parahippocampal gyrus. CONCLUSIONS: The comparison of the experimental results demonstrates that using a Gaussian pre-smoothing filter and VST produce the best results for the peak signal-to-noise ratio (PSNR) and atrophy detection.

[An approach for segmentation of X-ray angiographic image based on region-growing and structure inferring].

We presented a new method for vessel segmentation and vascular structure recognition for coronary angiographic images. During vessel segmentation, a new vessel function was proposed to attain vessel feature map. Then the region growing algorithm was implemented with an automatic selection of seed point, extraction of main vessel branch, and vessel detail repairing. In the algorithm of vascular structure recognition, a fuzzy operator was used, which can detect the structures of vascular segments, bifurcations, crosses, and tips. The experimental results indicated that there was about 5 percent larger vessel region which was extracted by the proposed segmentation method than that by the simple region growing algorithm, and several thinner vessels were resumed from the lower gray region. The results also indicated that the fuzzy operator could correctly infer the simulative and real vessel structure with 100% and 90.59% correctness rate on the average, respectively.

Automatic Delineation of the 3D Left Atrium From LGE-MRI: Actor-Critic Based Detection and Semi-Supervised Segmentation.

Accurate and automatic delineation of the left atrium (LA) is crucial for computer-aided diagnosis of atrial fibrillation-related diseases. However, effective model training typically requires a large amount of labeled data, which is time-consuming and labor-intensive. In this study, we propose a novel LA delineation framework. The region of LA is first detected using an actor-critic based deep reinforcement learning method with a shape-adaptive detection strategy using only box-level annotations, bypassing the need for voxel-level labeling. With the effectively detected LA, the impacts of class-imbalance and interference from surrounding tissues are significantly reduced. Subsequently, a semi-supervised segmentation scheme is coined to precisely delineate the contour of LA in 3D volume. The scheme integrates two independent networks with distinct structures, enabling implicit consistency regularization, capturing more spatial features, and avoiding the error accumulation present in current mainstream semi-supervised frameworks. Specifically, one network is combined with Transformer to capture latent spatial features, while the other network is based on pure CNN to capture local features. The difference prediction between these two sub-networks is exploited to mutually provide high-quality pseudo-labels and correct the cognitive bias. Experimental results on two public datasets demonstrate that our proposed strategy outperforms several state-of-the-art methods in terms of accuracy and clinical convenience.

SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation.

Image segmentation achieves significant improvements with deep neural networks at the premise of a large scale of labeled training data, which is laborious to assure in medical image tasks. Recently, semi-supervised learning (SSL) has shown great potential in medical image segmentation. However, the influence of the learning target quality for unlabeled data is usually neglected in these SSL methods. Therefore, this study proposes a novel self-correcting co-training scheme to learn a better target that is more similar to ground-truth labels from collaborative network outputs. Our work has three-fold highlights. First, we advance the learning target generation as a learning task, improving the learning confidence for unannotated data with a self-correcting module. Second, we impose a structure constraint to encourage the shape similarity further between the improved learning target and the collaborative network outputs. Finally, we propose an innovative pixel-wise contrastive learning loss to boost the representation capacity under the guidance of an improved learning target, thus exploring unlabeled data more efficiently with the awareness of semantic context. We have extensively evaluated our method with the state-of-the-art semi-supervised approaches on four public-available datasets, including the ACDC dataset, M&Ms dataset, Pancreas-CT dataset, and Task_07 CT dataset. The experimental results with different labeled-data ratios show our proposed method's superiority over other existing methods, demonstrating its effectiveness in semi-supervised medical image segmentation.

SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation.

Automated segmentation of liver tumors in CT scans is pivotal for diagnosing and treating liver cancer, offering a valuable alternative to labor-intensive manual processes and ensuring the provision of accurate and reliable clinical assessment. However, the inherent variability of liver tumors, coupled with the challenges posed by blurred boundaries in imaging characteristics, presents a substantial obstacle to achieving their precise segmentation. In this paper, we propose a novel dual-branch liver tumor segmentation model, SBCNet, to address these challenges effectively. Specifically, our proposed method introduces a contextual encoding module, which enables a better identification of tumor variability using an advanced multi-scale adaptive kernel. Moreover, a boundary enhancement module is designed for the counterpart branch to enhance the perception of boundaries by incorporating contour learning with the Sobel operator. Finally, we propose a hybrid multi-task loss function, concurrently concerning tumors' scale and boundary features, to foster interaction across different tasks of dual branches, further improving tumor segmentation. Experimental validation on the publicly available LiTS dataset demonstrates the practical efficacy of each module, with SBCNet yielding competitive results compared to other state-of-the-art methods for liver tumor segmentation.

Epidemiological/Disease and Economic Burdens of Cervical Cancer in 2010-2014: Are Younger Women at Risk?

OBJECTIVE: Cervical cancer is an important factor threatening women's health in China. This study examined the epidemiological and economic burden of cervical cancer among the medically insured population, which could provide data support for government departments to formulate policies. METHODS: All new cases of cervical cancer under the Urban Employee Basic Medical Insurance (UEBMI) plan in a provincial capital city in eastern China from 2010 to 2014 were collected. The Cox proportional hazard model was used to analyze the factors affecting the survival rates for cervical cancer. Outpatient and hospitalization expenses were used to assess the direct economic burden, and the Potential Years of Life Loss (PYLL) and potential economic loss were calculated by the direct method to assess indirect burden. RESULTS: During the observation period, there were 1115 new cases and 137 deaths. The incidence rate was 14.85/100,000 person years, the mortality was 1.82/100,000 person years, and the five-year survival rate was 75.3%. The age of onset was mainly concentrated in the 30-59 age group (82.9%) and the tendency was towards younger populations. The age of onset (HR = 1.037, 95% CI = 1.024-1.051), the frequency of hospitalization services (HR = 1.085, 95% CI = 1.061-1.109), and the average length of stay (ALOS) (HR = 1.020, 95% CI = 1.005-1.051) were the related factors affecting overall survival. Among the direct economic burden, the average outpatient cost was $4314, and the average hospitalization cost was $12,007. The average outpatient and hospitalization costs within 12 months after onset were $2871 and $8963, respectively. As for indirect burden, the average Potential Years of Life Loss (PYLL) was 27.95 years, and the average potential economic loss was $95,200. CONCLUSIONS: The epidemiological and economic burden reported in the study was at a high level, and the onset age of cervical patients gradually became younger. The age of onset, the frequency of hospitalization services and the ALOS of cervical cancer patients should be given greater attention. Policymakers and researchers should focus on the trend of younger onset age of cervical cancer and the survival situation within 12 months after onset. Early intervention for cervical cancer patients, particularly younger women, may help reduce the burden of cervical cancer.

Semi-supervised segmentation of coronary DSA using mixed networks and multi-strategies.

The coronary arteries supply blood to the myocardium, which originate from the root of the aorta and mainly branch into the left and right. X-ray digital subtraction angiography (DSA) is a technique for evaluating coronary artery plaques and narrowing, that is widely used because of its time efficiency and cost-effectiveness. However, automated coronary vessel classification and segmentation remains challenging using a little data. Therefore, the purpose of this study is twofold: one is to propose a more robust method for vessel segmentation, the other is to provide a solution that is feasible with a small amount of labeled data. Currently, there are three main types of vessel segmentation methods, i.e., graphical- and statistical-based; clustering theory based, and deep learning-based methods for pixel-by-pixel probabilistic prediction, among which the last method is the mainstream with high accuracy and automation. Under this trend, an Inception-SwinUnet (ISUnet) network combining the convolutional neural network and Transformer basic module was proposed in this paper. Considering that data-driven fully supervised learning (FSL) segmentation methods require a large set of paired data with high-quality pixel-level annotation, which is expertise-demanding and time-consuming, we proposed a Semi-supervised Learning (SSL) method to achieve better performance with a small amount of labeled and unlabeled data. Different from the classical SSL method, i.e., Mean-Teacher, our method used two different networks for cross-teaching as the backbone. Meanwhile, inspired by deep supervision and confidence learning (CL), two effective strategies for SSL were adopted, which were denominated Pyramid-consistency Learning (PL) and Confidence Learning (CL), respectively. Both were designed to filter the noise and improve the credibility of pseudo labels generated by unlabeled data. Compared with existing methods, ours achieved superior segmentation performance over other FSL and SSL ones by using data with a small equal number of labels. Code is available in https://github.com/Allenem/SSL4DSA.

Online Hard Patch Mining Using Shape Models and Bandit Algorithm for Multi-Organ Segmentation.

Hard sample selection can effectively improve model convergence by extracting the most representative samples from a training set. However, due to the large capacity of medical images, existing sampling strategies suffer from insufficient exploitation for hard samples or high time cost for sample selection when adopted by 3D patch-based models in the field of multi-organ segmentation. In this paper, we present a novel and effective online hard patch mining (OHPM) algorithm. In our method, an average shape model that can be mapped with all training images is constructed to guide the exploration of hard patches and aggregate feedback from predicted patches. The process of hard mining is formalized as a multi-armed bandit problem and solved with bandit algorithms. With the shape model, OHPM requires negligible time consumption and can intuitively locate difficult anatomical areas during training. The employment of bandit algorithms ensures online and sufficient hard mining. We integrate OHPM with advanced segmentation networks and evaluate them on two datasets containing different anatomical structures. Comparative experiments with other sampling strategies demonstrate the superiority of OHPM in boosting segmentation performance and improving model convergence. The results in each dataset with each network suggest that OHPM significantly outperforms other sampling strategies by nearly 2% average Dice score.

Guidewire simulation of endovascular intervention: A systematic review.

BACKGROUND: Endovascular intervention is an important minimally invasive surgery that requires professional skills to operate surgical instruments. Such skills are mainly gained through the traditional training paradigm of "see one, do one, teach one", rather than the guidewire simulation system. METHODS: To identify limitations of existing guidewire simulation research and suggest further research orientations, a comprehensive search on literature published from 2007 to 2021 is performed in 11 selected electronic databases. Through our scrutiny and filtration, 34 articles are selected as major studies for careful examinations. RESULTS: We identify challenges faced in the field of endovascular intervention guidewire simulation. We examine and classify guidewire simulation techniques (including guidewire models, collision detection methods and collision response methods), accuracy evaluation methods, error sources, and performance optimization methods. CONCLUSIONS: Guidewire simulation can satisfy the urgent need to train surgeons, thus more efforts should be dedicated enabling its wide application in clinical environment.

Towards real time guide wire shape extraction in fluoroscopic sequences: A two phase deep learning scheme to extract sparse curvilinear structures.

BACKGROUND AND OBJECTIVE: Real time localization and shape extraction of guide wire in fluoroscopic images plays a significant role in the image guided navigation during cerebral and cardiovascular interventions. Given the complexity of the non-rigid and sparse characteristics of guide wire structures, and the low SNR(Signal Noise Ratio) of fluoroscopic images, traditional handcrafted guide wire tracking methods such as Frangi filter, Hessian Matrix, or open active contour usually produce insufficient accuracy with high computational cost, and may require extra human intervention for proper initialization or correction. The application of deep learning techniques to guide wire tracking is reported to produce significant improvement in guide wire localization accuracy, but the heavy calculation cost is still a concern. METHOD: In this paper we propose a two phase deep learning scheme for accurate and real time guide wire shape extraction in fluoroscopic sequences. In the first phase we train a guide wire localization network to pick image regions containing guide wire structures. From the picked image regions, we train a guide wire shape extraction network in the second phase to mark the guide wire pixels. RESULTS: We report that our proposed method can accurately distinguish about 99% of the guide wire structure pixels, and the falsely detected pixels in the background are close to 0, the average offset from the ground truth is less than 1 pixel. For extreme cases where traditional handcrafted method may fail, our proposed method can still extract guide wire completely and accurately. The processing time for a 512 × 512 pixels image is 78 ms. CONCLUSION: Compared with the traditional filtering based method from our previous work, we show that our proposed method can achieve more accurate and stable performance. Compared with other deep learning methods, our proposed method significantly improve calculation efficiency to meet the real time requirement of clinical applications.

Synergistic Effects of TW-37 and ABT-263 on Renal Cell Carcinoma Cells.

BACKGROUND: Renal cell carcinoma (RCC) is a common urological system malignancy lack of effective therapeutic options. Upregulation of the Bcl-2 proteins was correlated with poor prognosis of RCC, suggesting that BH-3 mimetics may be a promising treatment option. ABT-263 is a BH3 mimetic that possesses anti-tumor effects. TW-37 is another inhibitor of Bcl-2 family protein with potential anti-tumor activities. However, since their effect as single agent is limited, combination treatment represents a strategy to improve the efficiency. We studied the ABT-263 in combination with TW-37 and analyzed the molecular mechanisms of action in RCC cells. METHODS: MTT and colony formation assays were used to measure the proliferation of RCC cells. Transwell assay was used to assay the migration and invasion of RCC cells. Cell cycle distribution and apoptosis were measured using the flow cytometry and apoptotic nucleosome assay, respectively. Western blotting was performed to measure the change of proteins. The anti-tumor effects of ABT-263, TW-37 and their combination were also evaluated in vivo. RESULTS: Cotreatment of TW-37 and ABT-263 synergistically repressed the proliferation of RCC cells. TW-37 and ABT-263 also synergistically inhibited the migration and invasion of RCC cells It was also showed that TW-37 and ABT-263 synergistically induced cell cycle arrest at the G2/M phase. Furthermore, increased apoptosis was observed after exposure to TW-37 and ABT-263. Mechanism investigation showed that TW-37 and ABT-263 synergistically induced apoptosis via the mitochondrial pathway and relied on the activation of Bax and caspases. Furthermore, ERK signaling pathway activation was detected after treated with TW-37 and ABT-263. Finally, TW-37 and ABT-263 also synergistically repressed the growth of RCC cells in xenograft mice. CONCLUSION: In summary, our data demonstrated that combined treatment with TW-37 and ABT-263 exhibited synergistic RCC cell death and this combination may be applied as an effective therapeutic strategy against RCC.

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, challenges of the manual procedure may degrade the efficacy of the technique, such as the high accuracy requirement and radiation exposure to the surgeons. This paper aims to develop a robotic system and its control methods for assisting surgeons on the treatment. METHOD: We present an interventional robotic system, which consists of a 5 Degree-of-Freedom (DoF) positioning robotic arm (a 3-DoF translational joint and a 2-DoF revolute joint) and a needle actuator used for needle insertion and radioactive seeds implantation. Control strategy is designed for the system to ensure the safety of the motion. In the designed framework, an artificial potential field (APF)-based motion planning and an ultrasound (US) image-based contacting methods are proposed for the control. RESULT: Experiments were performed to evaluate position and orientation accuracy as well as validate the motion planning procedure of the system. The mean and standard deviation of targeting error is 0.69 mm and 0.33 mm, respectively. Needle placement accuracy is 1.10 mm by mean. The feasibility of the control strategy, including path planning and the contacting methods, is demonstrated by simulation and experiments based on an abdominal phantom. CONCLUSION: This paper presents a robotic system with force and US image feedback in assisting surgeons performing brachytherapy on liver tumors. The proposed robotic system is capable of executing an accurate needle insertion task with by optical tracking. The proposed methods improve the safety of the robot's motion and automate the process of US probe contacting under the feedback of US-image.

Iterative stripe artifact correction framework for TOF-MRA.

The purpose of this study is to develop a practical stripe artifacts correction framework on three-dimensional (3-D) time-of-flight magnetic resonance angiography (TOF-MRA) obtained by multiple overlapping thin slab acquisitions (MOTSA) technology. In this work, the stripe artifacts in TOF-MRA were considered as a part of image texture. To separate the image structure and the texture, the relative total variation (RTV) was firstly employed to smooth the TOF-MRA for generating the template image with fewer image textures. Then a residual image was generated, which was the difference between the template image and the raw TOF-MRA. The residual image was served as the image texture, which contained the image details and stripe artifacts. Then, we obtained the artifact image from the residual image via a filter in a specific direction since the image artifacts appeared as stripes. The image details were then produced from the difference between the artifact image and the image texture. To produce the corrected images, we finally compensated the image details to the RTV smoothing image. The proposed method was continued until the stripe artifacts during the iteration vary as little as possible. The digital phantom and the real patients' TOF-MRA were used to test the approach. The spatial uniformity was increased from 74% to 82% and the structural similarity was improved from 86% to 98% in the digital phantom test by using the proposed algorithm. Our approach proved to be highly successful in eliminating stripe artifacts in real patient data tests while retaining image details. The proposed iterative framework on TOF-MRA stripe artifact correction is effective and appealing for enhancing the imaging performance of multi-slab 3-D acquisitions.

Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network.

Automated multi-organ abdominal Computed Tomography (CT) image segmentation can assist the treatment planning, diagnosis, and improve many clinical workflows' efficiency. The 3-D Convolutional Neural Network (CNN) recently attained state-of-the-art accuracy, which typically relies on supervised training with many manual annotated data. Many methods used the data augmentation strategy with a rigid or affine spatial transformation to alleviate the over-fitting problem and improve the network's robustness. However, the rigid or affine spatial transformation fails to capture the complex voxel-based deformation in the abdomen, filled with many soft organs. We developed a novel Hybrid Deformable Model (HDM), which consists of the inter-and intra-patient deformation for more effective data augmentation to tackle this issue. The inter-patient deformations were extracted from the learning-based deformable registration between different patients, while the intra-patient deformations were formed using the random 3-D Thin-Plate-Spline (TPS) transformation. Incorporating the HDM enabled the network to capture many of the subtle deformations of abdominal organs. To find a better solution and achieve faster convergence for network training, we fused the pre-trained multi-scale features into the a 3-D attention U-Net. We directly compared the segmentation accuracy of the proposed method to the previous techniques on several centers' datasets via cross-validation. The proposed method achieves the average Dice Similarity Coefficient (DSC) 0.852, which outperformed the other state-of-the-art on multi-organ abdominal CT segmentation results.

CT-based peritumoral radiomics signatures for malignancy grading of clear cell renal cell carcinoma.

OBJECTIVE: To evaluate the efficiency of CT-based peritumoral radiomics signatures of clear cell renal cell carcinoma (ccRCC) for malignancy grading in preoperative prediction. MATERIALS AND METHODS: 203 patients with pathologically confirmed as ccRCC were retrospectively enrolled in this study. All patients were categorized into training set (n = 122) and validation set (n = 81). For each patient, two types of volumes of interest (VOI) were masked on CT images. One type of VOIs was defined as the tumor mass volume (TMV), which was masked by radiologists delineating the outline of all contiguous slices of the entire tumor, while the other type defined as the peritumoral tumor volume (PTV), which was automatically created by an image morphological method. 1760 radiomics features were calculated from each VOI, and then the discriminative radiomics features were selected by Pearson correlation analysis for reproducibility and redundancy. These selected features were investigated their validity for building radiomics signatures by mRMR feature ranking method. Finally, the top ranked features, which were used as radiomics signatures, were input into a classifier for malignancy grading. The prediction performance was evaluated by receiver operating characteristic (ROC) curve in an independent validation cohort. RESULTS: The radiomics signatures of PTV showed a better performance on malignancy grade prediction of ccRCC with AUC of 0.807 (95% CI 0.800-0.834) in train data and 0.848 (95% CI 0.760-0.936) in validation data, while the radiomics signatures of TMV with AUC of 0.773 (95% CI 0.744-0.802) in train data and 0.810 (95% CI 0.706-0.914) in validation data. CONCLUSION: The CT-based peritumoral radiomics signature is a potential way to be used as a noninvasive tool to preoperatively predict the malignancy grades of ccRCC.

A Novel Ultrasound Probe Spatial Calibration Method Using a Combined Phantom and Stylus.

Intra-operative ultrasound (US) is a popular imaging modality for its non-radiative and real-time advantages. However, it is still challenging to perform an interventional procedure under two-dimensional (2-D) US image guidance. Accordingly, the trend has been to perform three-dimensional (3-D) US image guidance by equipping the US probe with a spatial position tracking device, which requires accurate probe calibration for determining the spatial position between the B-scan image and the tracked probe. In this report, we propose a novel probe spatial calibration method by developing a calibration phantom combined with the tracking stylus. The calibration phantom is custom-designed to simplify the alignment between the stylus tip and the B-scan image plane. The spatial position of the stylus tip is tracked in real time, and its 2-D image pixel location is extracted and collected simultaneously. Gaussian distribution is used to model the spatial position of the stylus tip and the iterative closest point-based optimization algorithm is used to estimate the spatial transformation that matches these two point sets. Once the probe is calibrated, its trajectory and the B-scan image are collected and used for the volume reconstruction in our freehand 3-D US imaging system. Experimental results demonstrate that the probe calibration approach results in less than 1-mm mean point reconstruction accuracy. It requires less than 5 min for an inexperienced user to complete the probe calibration procedure with minimal training. The mockup test shows that the 3-D images are geometrically correct with 0.28°-angle accuracy and 0.40-mm distance accuracy.

Statistical modeling and knowledge-based segmentation of cerebral artery based on TOF-MRA and MR-T1.

BACKGROUND AND OBJECTIVE: For cerebrovascular segmentation from time-of-flight (TOF) magnetic resonance angiography (MRA), the focused issues are segmentation accuracy, vascular network coverage ratio, and cerebral artery and vein (CA/CV) separation. Therefore, cerebral artery segmentation is a challenging work, while a complete solution is lacking so far. METHODS: The preprocessing of skull-stripping and Hessian-based feature extraction is first implemented to acquire an indirect prior knowledge of vascular distribution and shape. Then, a novel intensity- and shape-based Markov statistical modeling is proposed for complete cerebrovascular segmentation, where our low-level process employs a Gaussian mixture model to fit the intensity histogram of the skull-stripped TOF-MRA data, while our high-level process employs the vascular shape prior to construct the energy function. To regularize the individual data processes, Markov regularization parameter is automatically estimated by using a machine-learning algorithm. Further, cerebral artery and vein (CA/CV) separation is explored with a series of morphological logic operations, which are based on a direct priori knowledge on the relationship of arteriovenous topology and brain tissues in between TOF-MRA and MR-T1. RESULTS: We employed 109 sets of public datasets from MIDAS for qualitative and quantitative assessment. The Dice similarity coefficient, false negative rate (FNR), and false positive rate (FPR) of 0.933, 0.158, and 0.091% on average, as well as CA/CV separation results with the agreement, FNR, and FPR of 0.976, 0.041, and 0.022 on average. For clinical visual assessment, our methods can segment various sizes of the vessel in different contrast region, especially performs better on vessels of small size in low contrast region. CONCLUSION: Our methods obtained satisfying results in visual and quantitative evaluation. The proposed method is capable of accurate cerebrovascular segmentation and efficient CA/CV separation. Further, it can stimulate valuable clinical applications on the computer-assisted cerebrovascular intervention according to the neurosurgeon's recommendation.