MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用

目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。

3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.

3种3D打印模型辅助治疗RobinsonⅡB2型锁骨骨折

背景:随着3D打印技术在医学中的应用及发展,使得骨科内固定手术迈向精准化、个体化,通过3D打印技术获得的等比例骨折模型进行术前模拟、规划,实现了由传统的2D图像向更加形象、精细的立体实物的跨越,让术者提前了解骨折类型、预演复位顺序,进而实现骨折手术的个体化实施,优化了手术过程,带来更佳的术后恢复效果和更少的手术并发症。目的:比较3种3D打印模型结合计算机虚拟复位技术辅助切开复位接骨板内固定和传统切开复位接骨板内固定治疗RobinsonⅡB2型锁骨骨折的临床疗效。方法:将80例RobinsonⅡB2型锁骨骨折患者随机分为试验组(40例)和对照组(40例),试验组利用3种3D打印模型(患侧锁骨骨折模型、计算机模拟锁骨骨折复位后模型、健侧锁骨镜像模型)结合计算机虚拟复位技术在术前进行体外手术预演,最后利用健侧锁骨镜像模型进行3D打印来提前弯折和选择接骨板进行内固定,对照组直接进行切开复位接骨板内固定。比较两组患者入院至手术时间、术中出血量、手术时间、透视次数、对接骨板的折弯次数、骨折愈合时间、并发症发生情况及两组患者治疗前后目测类比评分、Constant肩关节功能评分。结果与结论:试验组患者入院至手术时间长于对照组(P<0.05);试验组患者手术时间、术中透视次数及对接骨板的折弯次数均小于对照组(P<0.05);试验组患者骨折愈合更快,并发症更少(P<0.05);两组患者术中出血量无统计学差异(P>0.05);两组患者Constant评分均有随时间延长而上升的趋势(F=613.50,P<0.001),但组间比较差异无显著性意义(F=0.08,P=0.78),测量次数与分组无交互效应(F=0.27,P=0.66)。两组患者目测类比评分随时间延长而下降(F=1149.55,P<0.001),但组间比较差异无显著性意义(F=0.02,P=0.88),测量次数与分组无交互效应(F=1.02,P=0.36)。结果表明使用3D打印模型结合计算机虚拟复位技术进行术前预演,可以缩短手术时间、减少术中透视及对接骨板折弯的次数,同时具有骨折愈合更快、并发症更少的优势,并能达到与传统切开复位接骨板内固定相似的功能恢复。

荧光分子断层成像与计算机断层成像的2D/3D配准方法研究

针对荧光分子断层成像(fluorescence molecular tomography,FMT)中的二维光学图像与三维计算机断层成像(computed tomography,CT)图像在维度和风格等方面的差异导致配准困难的问题,本研究提出了一种基于任务回归粗配准及梯度下降精配准的两阶段配准方法。在粗配准阶段,本研究提出了目标姿态预测模型RR-Net,实现对初始位姿参数的快速估计;在精配准阶段,基于可微成像渲染模块,在粗配准参数的基础上进行迭代训练,进一步提高配准精度。本研究在公开小鼠数据集及实验室自采数据共238140张轮廓图像中验证了该配准方法的有效性。实验结果显示,本研究提出的2D/3D配准方法在FMT光学图像和CT图像配准中,相似系数为0.96±0.03,平均目标配准误差为(1.14±0.83)mm。该方法的配准效果及稳定性均优于传统方法。

2D/3D级联卷积在分割CT肺动脉上的应用研究

医学影像分割是计算机辅助诊断的重要组成部分。针对CT影像的三维特性,提出了一种基于2D/3D级联卷积的Unet网络结构用来分割肺动脉。该结构相比基于传统2D卷积的方法,关联了第三维度信息,提高了分割准确度和泛化能力,相比基于传统3D卷积的方法提高了准确度和执行效率。实验对多套肺动脉增强CT数据集做了验证,分割准确率达到85.7%,高于传统2D和3DUnet网络,同时执行效率较3DUnet提高近30%,在CT影像分割上做到了效率和准确度的兼顾。

A new projection model based robust 2D-3D registration method on Fourier-Mellin space for image guided intervention

An automatic method is proposed to solve the registration problem,which aligns a single 2D fluoroscopic image to a 3D image volume without demanding any additional media like calibration plate or user interactions.First,a mathematic projection model is designed which can reduce the influence of projection distortion on parameter optimization and improve the registration accuracy.Then,a two stage optimization method is proposed,which enables a robust registration in a wide parameter space.Furthermore,an automatic registration framework is proposed based on the FourierMellin robust image comparison descriptor.Experimental results show that the registration method has a high accuracy with average rotation error of 0.6 degree and average translation error of 1.4mm.

Drishti Paint 3.2:a new open-source tool for both 2D and 3D segmentation

X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

The diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice

Objective: Computerized tomography (CT) plays an important role in the diagnosis of diseases of biliary tract. Recently, three dimensions (3D) spiral CT imaging has been used in surgical diseases gradually. This study was designed to evaluate the diagnostic value of 3D spiral CT imaging of cholangiopancreatic ducts on obstructive jaundice. Methods: Thirty patients with obstructive jaundice had received B-mode ultrasonography, CT, percutaneous transhepatic cholangiography (PTC) or endoscopic retrograde cholangiopancreatography (ERCP), and 3D spiral CT imaging of cholangiopancreatic ducts preoperatively. Then the diagnose accordance rate of these examinational methods were compared after operations. Results: The diagnose accordance rate of 3D spiral CT imaging of cholangiopancreatic ducts was higher than those of B-mode ultrasonography, CT, or single PTC or ERCP, which showed clear images of bile duct tree and pathological changes. As to malignant obstructive jaundice, this examinational technique could clearly display the adjacent relationship between tumor and liver tissue, biliary ducts, blood vessels, and intrahepatic metastases. Conclusion: 3D spiral CT imaging of cholangiopancreatic ducts has significant value for obstructive diseases of biliary ducts, which provides effective evidence for the feasibility of tumor-resection and surgical options.

CT平扫的2D与3D影像组学模型诊断卵巢良恶性病变价值

目的:分析CT平扫的2D与3D影像组学模型诊断卵巢良恶性病变价值。方法:回顾性收集2017年7月-2022年7月本院收治的经病理学证实的卵巢良恶性病变患者100例临床资料,根据病理学结果分为恶性组(n=30)和良性组(n=70),CT平扫图像上勾画2D、3D肿瘤感兴趣区并提取图像特征,按照7:3的比例随机分层分为训练集(n=70)与验证集(n=30),提取CT影像组学特征,多因素logistic回归构建2D与3D影像组学模型,采用受试者工作特征(ROC)曲线评估2D与3D影像组学模型对卵巢良恶性病变的诊断效能并比较。结果:以肿块形态、肿瘤囊实性、腹水作为构建2D影像学特征模型,该模型训练集诊断卵巢良恶性病变的敏感度、特异度、曲线下面积(AUC)为88.9%、77.0%、0.86;验证集诊断卵巢良恶性病变的敏感度、特异度、AUC为81.8%、78.9%、0.82。以肿块形态、肿瘤囊实性、边界、腹水作为构建3D影像学特征模型,该模型训练集诊断卵巢良恶性病变的敏感度、特异度、AUC为90.7%、76.7%、0.86;验证集诊断敏感度、特异度、AUC为81.8%、73.7%、0.86。2D与3D影像组学模型诊断卵巢良恶性病变的敏感度、特异度及AUC未见差异(P>0.05)。结论:基于CT平扫的2D与3D影像组学模型诊断卵巢良恶性病变价值相当且均较高,但考虑到影像组学特征计算成本,更推荐使用2D影像组学模型。

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

A study of the effect of Sudan I, III, and IV on the DNA/RNA ratio and 3D structure of HepG-2 using LCM

To explore the effect of sudan Ⅰ, Ⅲ, and Ⅳ on the DNA/RNA ratio and changes in the 3D structure of HepG-2. LCM and 3D images are used to detect the DNA/RNA ratio and changes in the 3D structure of HepG-2 when treated with different dosages of sudan Ⅰ, Ⅲ, and Ⅳ. The DNA/RNA ratio of the control group is 1. 223 2 ±0. 084 4, while the fluorescence intensity of DNA in HepG-2 treated with sudan Ⅰ, Ⅲ, and Ⅳ is markedly greater than that of RNA, with the low-dosage group showing significant effect （P 〈 0. 01 ）, yielding DNA/RNA ratios of 1. 609 6 ±0. 199 0, 1. 445 5 ±0. 163 3, 1. 708 1 ±0. 109 0 respectively; 3D images show that DNA fluorescence in HepG-2 is mostly concentrated in the nuclear region, and is denser and stronger than RNA fluorescence. The DNA/RNA ratio of a treated group increases after being treated with different dosages of sudan, but it declines with increasing dosage, and within a certain dosage range, sudan Ⅰ, Ⅲ, and Ⅳ are shown to promote the growth of HepG-2.

PC-BASED SURFACE RECONSTRUCTION OF MEDICAL CT IMAGES

It is an active research area to reconstruct 3-D object and display its visible surfacesfrom cross-sectional images. In this paper, the methods of reconstructing 3-D object from medicalCT images and displaying the visible surfaces are discussed. A polygon approximation methodthat forms polygon with the same number of segment points and a fast interpolation method forcross-sectional contours are presented at first. Then the voxel set of a human liver is reconstructed.And then the liver voxel set is displayed using depth and gradient shading methods. The softwareis written in C programming language at a microcomputer image processing system with a PC/ATcomputer as the host and a PC-VISION board as the image processing unit. The result of theprocessing is satisfying.

Research on Automatic Elimination of Laptop Computer in Security CT Images Based on Projection Algorithm and YOLOv7-Seg

In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.

Clinical application of improved 2D computer-assisted fluoroscopic navigation through simulating a 3D vertebrae image to guide pedicle screw internal fixation

Objective To study the effect of using improved 2D computer-assisted fluoroscopic navigation through simulating 3D vertebrae image to guide pedicle screw internal fixation.Methods Posterior pedicle screw internal fixation,distraction

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

ASME 2013 NDE中有关相控阵超声成像检测的要点评析 第二部分:计算机成像技术

探讨ASME最新版(2013)第Ⅴ卷《无损检测》第四章中有关承压设备相控阵超声成像检测的主要规定。评析承压设备焊接接头体积检测必须采用的CI(计算机成像)技术,突出相控阵超声检测扫查的典型模式,探测布图的典型示例,以及典型缺陷的相控阵读谱精要。意在对照国标国情,找差距、纠偏误,使中国企业正确执行ASME有关规范的水平更上一个台阶。

基于T2DR-Net和互信息的光学-CT图像配准方法研究

荧光分子断层成像技术(fluorescence molecular tomography,FMT)系统中为获得体内光源的结构信息,需要利用CT体数据。FMT系统在进行光学图像与CT图像的配准时,由于两种模态图像的成像原理、图像风格和图像维度等方面的差异,导致传统配准方法耗时长、效果差。本研究提出了一种基于T2DR-Net(texture transfer and dense registration net)与互信息的光学-CT图像配准方法,实现FMT系统中白光图像与CT图像的配准。该方法将光学-CT图像配准分为粗配准和精配准两个部分。在粗配准阶段,利用CycleGAN实现了FMT白光图像和CT投影像的纹理迁移,以降低两种图像纹理差异对图像配准的影响,并提出了DenseReg-Net模型获取白光图像和CT投影像粗配准参数;在精配准阶段,通过互信息方法进一步对两种模态图像配准,并得到最终的配准结果。利用1330张光学图像和39711张CT投影像作为样本集来验证配准方法的有效性,实验结果表明,本研究提出的光学-CT图像配准方法,相关系数为0.8797±0.0175,结构相似性为0.8683±0.0051,模型配准时间为(2.88±1.39)s。模型的配准效果及其稳定性优于传统方法。此外,与传统方法相比,速度提升了约60倍。

A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.