Computer Assisted Surgery最新文献

{"title":"Lung segmentation method with dilated convolution based on VGG-16 network","authors":"Lei Geng, Siqi Zhang, Jun Tong, Zhitao Xiao","doi":"10.1080/24699322.2019.1649071","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649071","url":null,"abstract":"Abstract Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accurately segmenting the lung parenchyma, this paper proposes a segmentation method based on the combination of VGG-16 and dilated convolution. First of all, we use the first three parts of VGG-16 network structure to convolution and pooling the input image. Secondly, using multiple sets of dilated convolutions make the network has a large enough receptive field. Finally, the multi-scale convolution features are fused, and each pixel is predicted using MLP to segment the parenchymal region. Experimental results were produced over state of the art on 137 images which key metrics Dice similarity coefficient (DSC) is 0.9867. Experimental results show that this method can effectively segment the lung parenchymal area, and compared to other conventional methods better.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"27 - 33"},"PeriodicalIF":2.1,"publicationDate":"2019-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43876257","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":"Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis","authors":"Jue Zhang, Li Chen","doi":"10.1080/24699322.2019.1649074","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649074","url":null,"abstract":"Abstract To overcome the two-class imbalanced classification problem existing in the diagnosis of breast cancer, a hybrid of Random Over Sampling Example, K-means and Support vector machine (RK-SVM) model is proposed which is based on sample selection. Random Over Sampling Example (ROSE) is utilized to balance the dataset and further improve the diagnosis accuracy by Support Vector Machine (SVM). As there is one different sample selection factor via clustering that encourages selecting the samples near the class boundary. The purpose of clustering here is to reduce the risk of removing useful samples and improve the efficiency of sample selection. To test the performance of the new hybrid classifier, it is implemented on breast cancer datasets and the other three datasets from the University of California Irvine (UCI) machine learning repository, which are commonly used datasets in class imbalanced learning. The extensive experimental results show that our proposed hybrid method outperforms most of the competitive algorithms in term of G-mean and accuracy indices. Additionally, experimental results show that this method also performs superiorly for binary problems.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"62 - 72"},"PeriodicalIF":2.1,"publicationDate":"2019-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42793989","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":"Novel joint algorithm based on EEG in complex scenarios","authors":"Dong-Wei Chen, Wei-Qi Yang, Rui Miao, Lan Huang, Liu Zhang, Chunjian Deng, Na Han","doi":"10.1080/24699322.2019.1649078","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649078","url":null,"abstract":"Abstract At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"117 - 125"},"PeriodicalIF":2.1,"publicationDate":"2019-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47383231","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":"Automatic segmentation of arterial tree from 3D computed tomographic pulmonary angiography (CTPA) scans","authors":"Chi Zhang, Mingxia Sun, Yinan Wei, Hao Zhang, S. Xie, Tongxi Liu","doi":"10.1080/24699322.2019.1649077","DOIUrl":"https://doi.org/10.1080/24699322.2019.1649077","url":null,"abstract":"Abstract Pulmonary embolism (PE) and other pulmonary vascular diseases, have been found associated with the changes in arterial morphology. To detect arterial changes, we propose a novel, fully automatic method that can extract pulmonary arterial tree in computed tomographic pulmonary angiography (CTPA) images. The approach is based on the fuzzy connectedness framework, combined with 3D vessel enhancement and Harris Corner detection to achieve accurate segmentation. The effectiveness and robustness of the method is validated in clinical datasets consisting of 10 CT angiography scans (6 without PE and 4 with PE). The performance of our method is compared with manual classification and machine learning method based on random forest. Our method achieves a mean accuracy of 92% when compared to manual reference, which is higher than the 89% accuracy achieved by machine learning. This performance of the segmentation for pulmonary arteries may provide a basis for the CAD application of PE.","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"24 1","pages":"79 - 86"},"PeriodicalIF":2.1,"publicationDate":"2019-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24699322.2019.1649077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45698765","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":"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":"24 1","pages":"20 - 26"},"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":"24 1","pages":"12 - 3"},"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":"24 1","pages":"43 - 53"},"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":"24 1","pages":"1 - 2"},"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":"24 1","pages":"12 - 5"},"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":"21 2","pages":"1-8"},"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}