Computer Assisted Surgery最新文献

{"title":"Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT","authors":"Jinke Wang, Hongliang Zu, Haoyan Guo, R. Bi, Yuanzhi Cheng, S. Tamura","doi":"10.1080/24699322.2019.1649076","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649076","url":null,"abstract":"Abstract Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48854368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the time-velocity curve in phase-contrast magnetic resonance imaging: a phantom study","authors":"Jieun Park, Junghun Kim, Yongmin Chang, S. Youn, Hui-Joong Lee, E. Kang, Ki-Nam Lee, V. Suchánek, S. Hyun, Jongmin Lee","doi":"10.1080/24699322.2019.1649066","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649066","url":null,"abstract":"Abstract The aim of this study was to analyze the characteristics of time-velocity curve acquired by phase-contrast magnetic resonance imaging (PC-MRI) using an in-vitro flow model as a reference for hemodynamic studies. The time- velocity curves of the PC-MRI were compared with Doppler ultrasonography (US) and also compared with those obtained in the electromagnetic flowmeter. The correlation between techniques was analyzed using an electromagnetic flowmeter as a reference standard; the maximum, minimum, and average velocities, full-width at half-maximum (FWHM), and ascending gradient (AG) were measured from time-velocity curves. The correlations between an electromagnetic flowmeter and the respective measurement technique for the PC-MRI and Doppler US were found to be high (mean R2 > 0.9, p < 0.05). These results indicate that these measurement techniques are useful for measuring blood flow information and reflect actual flow. The PC-MRI was the best fit for the minimum velocity and FWHM, and the maximum velocity and AG were the best fit for Doppler US. The PC-MRI showed lower maximum velocity value and higher minimum velocity value than Doppler US. Therefore, PC-MRI demonstrates more obtuse time-velocity curve than Doppler US. In addition, the time- velocity curve of PC-MRI could be calibrated by introducing formulae that can convert each measurement value to a reference standard value within a 10% error. The PC-MRI can be used to estimate the Doppler US using this formula.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48739819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A visible human body slice segmentation method framework based on OneCut and adjacent image geometric features","authors":"B. Liu, Simei Li, Jingyi Zhang, Qian Wu, Liang Yang, Wen Qi, Sijie Guan, Shuo Zhang, Jianxin Zhang","doi":"10.1080/24699322.2019.1649068","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649068","url":null,"abstract":"Abstract As a recent research hot issue, obtaining the accurate 3 D organ models of Visible Human Project (VHP) has many significances. Therefore, how to extract the organ regions of interest (ROI) in the large-scale color slice image data set has become an urgent issue to be solved. In this paper, we propose a method framework based on OneCut algorithm and adjacent image geometric features to continuously extract the main organ regions is proposed. This framework mainly contains two parts: firstly, the OneCut algorithm is used to segment the ROI of target organ in the current image; secondly, the foreground image (obtained ROI) is corroded into several seed points and the background image (other region except for ROI) is refined into a skeleton. Then the obtained seed points and skeleton can be transmitted and mapped onto the next image as the input of OneCut algorithm. Thereby, the serialized slice images can be processed continuously without manual delineating. The experimental results show that the extracted VHP organs are satisfactory. This method framework may provide well technic foundation for other related application.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48162731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in computer-aided medical systems and clinical measurement","authors":"Chengyu Liu, L. Pan","doi":"10.1080/24699322.2019.1649079","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649079","url":null,"abstract":"","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42937074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of soft tissue thermal damage based on GPU acceleration","authors":"Jinao Zhang, J. Hills, Y. Zhong, B. Shirinzadeh, Julian Smith, Chengfan Gu","doi":"10.1080/24699322.2018.1557891","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557891","url":null,"abstract":"Abstract Hyperthermia treatments require precise control of thermal energy to form the coagulation zones which sufficiently cover the tumor without affecting surrounding healthy tissues. This has led modeling of soft tissue thermal damage to become important in hyperthermia treatments to completely eradicate tumors without inducing tissue damage to surrounding healthy tissues. This paper presents a methodology based on GPU acceleration for modeling and analysis of bio-heat conduction and associated thermal-induced tissue damage for prediction of soft tissue damage in thermal ablation, which is a typical hyperthermia therapy. The proposed methodology combines the Arrhenius Burn integration with Pennes’ bio-heat transfer for prediction of temperature field and thermal damage in soft tissues. The problem domain is spatially discretized on 3-D linear tetrahedral meshes by the Galerkin finite element method and temporally discretized by the explicit forward finite difference method. To address the expensive computation load involved in the finite element method, GPU acceleration is implemented using the High-Level Shader Language and achieved via a sequential execution of compute shaders in the GPU rendering pipeline. Simulations on a cube-shape specimen and comparison analysis with standalone CPU execution were conducted, demonstrating the proposed GPU-accelerated finite element method can effectively predict the temperature distribution and associated thermal damage in real time. Results show that the peak temperature is achieved at the heat source point and the variation of temperature is mainly dominated in its direct neighbourhood. It is also found that by the continuous application of point-source heat energy, the tissue at the heat source point is quickly necrotized in a matter of seconds, while the entire neighbouring tissues are fully necrotized in several minutes. Further, the proposed GPU acceleration significantly improves the computational performance for soft tissue thermal damage prediction, leading to a maximum reduction of 55.3 times in computation time comparing to standalone CPU execution.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1557891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43835146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity.","authors":"Fang Zhang,Yue Wu,Zhitao Xiao,Lei Geng,Jun Wu,Jia Wen,Wen Wang,Ping Liu","doi":"10.1080/24699322.2018.1557906","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557906","url":null,"abstract":"To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A targeting method for robot-assisted percutaneous needle placement under fluoroscopy guidance.","authors":"Zhonghao Han,Keyi Yu,Lei Hu,Weishi Li,Huilin Yang,Minfeng Gan,Na Guo,Biao Yang,Hongsheng Liu,Yuhan Wang","doi":"10.1080/24699322.2018.1560085","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560085","url":null,"abstract":"BACKGROUNDMinimally invasive procedures are rapidly growing in popularity thanks to advancements in medical robots, visual navigation and space registration techniques. This paper presents a precise and efficient targeting method for robot-assisted percutaneous needle placement under C-arm fluoroscopy.METHODA special end-effector was constructed to perform fluoroscopy calibration and robot to image-space registration simultaneously and automatically. In addition, formulations were given to compute the movement of robot targeting and evaluate targeting accuracy using only one X-ray image.RESULTA pre-clinical experiment showed that the maximum angle error was 0.94° and the maximum position error of a target located 80 mm below the end-effector was 1.31 mm. And evaluation of the system in a robot-assisted pedicle screws placement surgery has justified the accuracy and reliability of proposed method in clinical applications.CONCLUSIONThe positioning accuracy was increased by using present method. Moreover, radiation exposure and operation time were reduced significantly compared to other commonly used methods.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138509402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of the precuneus and posterior cingulate cortex in the neural routes to action.","authors":"Zijian Wang,Fei Liu,Yaoru Sun,Jie Li,Fang Wang,Zheng Lu","doi":"10.1080/24699322.2018.1560098","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560098","url":null,"abstract":"Neural substrates of action to the object or this specific direct route, however, remain unclear, especially for the connection from the visual pathway to the motor cortex. The study examined this issue by conducting an fMRI experiment, in which two action generation tasks involving pictures of real objects (PA) and the object's nouns (NA) were used, with pictures naming (PN) and covert noun reading (NR) being the control tasks. The result showed that the model predefined for the PCC and precuneus connecting IPL to the posterior-medial frontal cortex dominated over the others (with 0.45 probability), suggesting that the PCC and the precuneus locate at the neural substrates of action to the object. Furthermore, a feasibility study suggests that the neural pathway composed of the V3/MT, precuneus, PCC, and PM (premotor cortex) forms the direct route from perception to action, which also links to the dorsal pathway so that the perception of objects bypasses the semantic ventral pathway and then directly cues actions via the affordance.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis and recognition of characteristics of digitized tongue pictures and tongue coating texture based on fractal theory in traditional Chinese medicine.","authors":"Ji Zhang,Jun Qian,Tao Yang,Hai-Yan Dong,Rui-Juan Wang","doi":"10.1080/24699322.2018.1557890","DOIUrl":"https://doi.org/10.1080/24699322.2018.1557890","url":null,"abstract":"Simple fractal dimensions have been proposed for use in the analysis of the characteristics of digitized tongue pictures and tongue coating texture, which could further the establishment of objectified classification criteria under the conditions of expanding sample size. However, detailed descriptions on simple fractal dimensions have been limited. Therefore, BP (back propagation) neural network model classifiers could be designed by further calculation of the multiple fractal spectrum characteristics of digitized tongue pictures in order to classify and recognize the thin/thick or greasy characteristics of tongue coating.The fractal dimensions of sample data of 587 digitized tongue pictures were collected in a standard environment. A statistical analysis was conducted on the calculation results of the sample data, and the sensitivity of the fractal dimensions to the thin/thick and greasy characteristics of digitized tongue pictures was observed. As the overlap region resulted from a range of values of a single parameter, another eight characteristic parameters of the multiple fractal spectra of the digitized tongue pictures were further proposed as the elements in the input layer of the three-layers BP neural network. Automatic recognition classifiers were designed and trained for the characteristics of digitized tongue pictures and tongue coating textures.The simple fractal dimension was sensitive to the thin/thick and greasy characteristics of digitized tongue pictures and could better judge the characteristics of the thickness of the tongue coating. A classifier with characteristic parameters of multiple fractal spectra as the input vectors identified by the BP neural network models could effectively increase the accuracy rate judged by the characteristics of the tongue coating texture.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138530080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physiological interference reduction for near infrared spectroscopy brain activity measurement based on recursive least squares adaptive filtering and least squares support vector machines.","authors":"Xin Liu, Yan Zhang, Dan Liu, Qisong Wang, Ou Bai, Jinwei Sun, P. Rolfe","doi":"10.1080/24699322.2018.1560095","DOIUrl":"https://doi.org/10.1080/24699322.2018.1560095","url":null,"abstract":"Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2018.1560095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60126834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}