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  • 51.
    Wang, Chunliang
    et al.
    Linköping University Hospital.
    Smedby, Örjan
    Linköping University Hospital.
    Coronary artery segmentation and skeletonization based on competing fuzzy connectedness tree2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, Berlin, Heidelberg, 2007, p. 311-318Conference paper (Refereed)
    Abstract [en]

    We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.

  • 52.
    Wang, Chunliang
    et al.
    Linköping University, Sweden.
    Smedby, Örjan
    Linköping University, Sweden.
    Fully automatic brain segmentation using model-guided level sets and skeleton-based models2013Conference paper (Refereed)
    Abstract [en]

    A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

  • 53.
    Wang, Chunliang
    et al.
    Linköping University, Linköping, Sweden.
    Smedby, Örjan
    Linköping University, Linköping, Sweden.
    Integrating automatic and interactive methods for coronary artery segmentation: let the PACS workstation think ahead2010In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 5, no 3, p. 275-285Article in journal (Refereed)
    Abstract [en]

    Purpose To present newly developed software that can provide fast coronary artery segmentation and accurate centerline extraction for later lesion visualization and quantitative measurement while minimizing user interaction. Methods Previously reported fully automatic and interactive methods for coronary artery extraction were optimized and integrated into a user-friendly workflow. The user's waiting time is saved by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides an intuitive interactive analysis environment. Results The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 1.4-2.5 min as a single-thread application in the background. Interactive processing takes 3 min in average. Conclusion In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.

  • 54.
    Wang, Chunliang
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    Model-based left ventricle segmentation in 3D ultrasound using phase image2014Conference paper (Refereed)
  • 55.
    Wang, Chunliang
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Linköping University.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Linköping University.
    Multiorgan Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information2017In: Cloud-Based Benchmarking of Medical Image Analysis / [ed] Allan Hanbury, Henning Müller, Georg Langs, Cham: Springer, 2017, p. 165-183Chapter in book (Other academic)
    Abstract [en]

    In this chapter, we introduce an automatic multiorgan segmentation method using a hierarchical-shape-prior-guided level set method. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that the children structures are always contained by the parent structure. This hierarchical approach solves two challenges of multiorgan segmentation. First, it gradually refines the prediction of the organs’ position by locating and segmenting the larger parent structure. Second, it solves the ambiguity of boundary between two attaching organs by looking at a large scale and imposing the additional shape constraint of the higher-level structures. To improve the segmentation accuracy, a model-guided local phase term is introduced and integrated with the conventional region-based energy function to guide the level set propagation. Finally, a novel coherent propagation method is implemented to speed up the model-based level set segmentation. In the VISCERAL Anatomy challenge, the proposed method delivered promising results on a number of abdominal organs.

  • 56.
    Wang, Chunliang
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden .
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Multi-organ Segmentation Using Shape Model Guided Local Phase Analysis2015In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III / [ed] Navab, Hornegger, Wells and Frangi, Springer, 2015, p. 149-156Chapter in book (Refereed)
    Abstract [en]

    To improve the accuracy of multi-organ segmentation, we propose a model-based segmentation framework that utilizes the local phase information from paired quadrature filters to delineate the organ boundaries. Conventional local phase analysis based on local orientation has the drawback of outputting the same phases for black-to-white and white-to-black edges. This ambiguity could mislead the segmentation when two organs’ borders are too close. Using the gradient of the signed distance map of a statistical shape model, we could distinguish between these two types of edges and avoid the segmentation region leaking into another organ. In addition, we propose a level-set solution that integrates both the edge-based (represented by local phase) and region-based speed functions. Compared with previously proposed methods, the current method uses local adaptive weighting factors based on the confidence of the phase map (energy of the quadrature filters) instead of a global weighting factor to combine these two forces. In our preliminary studies, the proposed method outperformed conventional methods in terms of accuracy in a number of organ segmentation tasks.

  • 57.
    Wang, Chunliang
    et al.
    KTH, School of Technology and Health (STH).
    Wang, Q.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Automatic heart and vessel segmentation using random forests and a local phase guided level set method2017In: Reconstruction, Segmentation, and Analysis Of Medical Images, Springer Verlag , 2017, Vol. 10129, p. 159-164Conference paper (Refereed)
    Abstract [en]

    In this report, a novel automatic heart and vessel segmentation method is proposed. The heart segmentation pipeline consists of three major steps: heart localization using landmark detection, heart isolation using statistical shape model and myocardium segmentation using learning based voxel classification and local phase analysis. In our preliminary test, the proposed method achieved encouraging results.

  • 58. Zheng, Guoyan
    et al.
    Chu, Chengwen
    Belav‵y, Daniel L
    Ibragimov, Bulat
    Korez, Robert
    Vrtovec, Toma\vz
    Hutt, Hugo
    Everson, Richard
    Meakin, Judith
    Andrade, Isabel L\uopez
    Wang, Chunliang
    Sectra, Linköping, Sweden.
    Li, Shuo
    Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge2017In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 35, p. 327-344Article in journal (Refereed)
    Abstract [en]

    The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8 mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1 mm and a mean Hausdorff distance of 4.3 mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.

  • 59. Zhuang, Xiahai
    et al.
    Li, Lei
    Payer, Christian
    Štern, Darko
    Urschler, Martin
    Heinrich, Mattias P
    Oster, Julien
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Bian, Cheng
    Yang, Xin
    Heng, Pheng-Ann
    Mortazi, Aliasghar
    Bagci, Ulas
    Yang, Guanyu
    Sun, Chenchen
    Galisot, Gaetan
    Ramel, Jean-Yves
    Brouard, Thierry
    Tong, Qianqian
    Si, Weixin
    Liao, Xiangyun
    Zeng, Guodong
    Shi, Zenglin
    Zheng, Guoyan
    Wang, Chengjia
    MacGillivray, Tom
    Newby, David
    Rhode, Kawal
    Ourselin, Sebastien
    Mohiaddin, Raad
    Keegan, Jennifer
    Firmin, David
    Yang, Guang
    Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.2019In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 58, article id 101537Article in journal (Refereed)
    Abstract [en]

    Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

12 51 - 59 of 59
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  • modern-language-association-8th-edition
  • vancouver
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  • Other locale
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