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  • 51. Pavoni, Marco
    et al.
    Chang, Yongjun
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Park, Sang-Ho
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention2018In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 5, no 2, article id 024006Article in journal (Refereed)
    Abstract [en]

    Percutaneous coronary intervention (PCI) uses x-ray images, which may give high radiation dose and high concentrations of contrast media, leading to the risk of radiation-induced injury and nephropathy. These drawbacks can be reduced by using lower doses of x-rays and contrast media, with the disadvantage of noisier PCI images with less contrast. Vessel-edge-preserving convolutional neural networks (CNN) were designed to denoise simulated low x-ray dose PCI images, created by adding artificial noise to high-dose images. Objective functions of the designed CNNs have been optimized to achieve an edge-preserving effect of vessel walls, and the results of the proposed objective functions were evaluated qualitatively and quantitatively. Finally, the proposed CNN-based method was compared with two state-of-the-art denoising methods: K-SVD and block-matching and 3D filtering. The results showed promising performance of the proposed CNN-based method for PCI image enhancement with interesting capabilities of CNNs for real-time denoising and contrast enhancement tasks.

  • 52.
    Pavoni, Marco
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Politecnico di Torino, Italy.
    Chang, Yongjun
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Image denoising with convolutional neural networks for percutaneous transluminal coronary angioplasty2018In: VipIMAGE 2017: Proceedings of the VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing Porto, Portugal, October 18-20, 2017, Springer Netherlands, 2018, Vol. 27, p. 255-265Conference paper (Refereed)
    Abstract [en]

    Percutaneous transluminal coronary angioplasty (PTCA) requires X-ray images employing high radiation dose with high concentration of contrast media, leading to the risk of radiation induced injury and nephropathy. These drawbacks can be reduced by using lower doses of X-rays and contrast media, with the disadvantage of noisier PTCA images. In this paper, convolutional neural networks were used in order to denoise low dose PTCA-like images, built by adding artificial noise to high dose images. MSE and SSIM based loss functions were tested and compared visually and quantitatively for different types and levels of noise. The results showed promising performance for denoising task.

  • 53. Petersson, Helge
    et al.
    Sinkvist, David
    Wang, Chunliang
    Linköping University, SE-581 85 Linköping, Sweden.
    Smedby, Örjan
    Linköping University, SE-581 85 Linköping, Sweden.
    Web-based interactive 3D visualization as a tool for improved anatomy learning2009In: Anatomical Sciences Education, ISSN 1935-9772, Vol. 2, no 2, p. 61-68Article in journal (Refereed)
    Abstract [en]

    Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain from its use in reaching their anatomical learning objectives. Several 3D vascular VR models were created using an interactive segmentation tool based on the "virtual contrast injection" method. This method allows users, with relative ease, to convert computer tomography or magnetic resonance images into vivid 3D VR movies using the OsiriX software equipped with the CMIV CTA plug-in. Once created using the segmentation tool, the image series were exported in Quick Time Virtual Reality (QTVR) format and integrated within a web framework of the Educational Virtual Anatomy (EVA) program. A total of nine QTVR movies were produced encompassing most of the major arteries of the body. These movies were supplemented with associated information, color keys, and notes. The results indicate that, in general, students' attitudes towards the EVA-program were positive when compared with anatomy textbooks, but results were not the same with dissections. Additionally, knowledge tests suggest a potentially beneficial effect on learning.

  • 54.
    Platten, Michael
    et al.
    KTH.
    Chowdhury, Manish
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Imaging.
    Estimation of trabecular thickness in grayscale: an in vivo study2017In: ESSR 2017 / P-0196, 2017Conference paper (Refereed)
  • 55. Saffari, S. Ehsan
    et al.
    Love, Askell
    Fredrikson, Mats
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Regression models for analyzing radiological visual grading studies - an empirical comparison2015In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 15, article id 49Article in journal (Refereed)
    Abstract [en]

    Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. Results: In general, the goodness of fit (AIC and McFadden's Pseudo R-2) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R-2 was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.

  • 56. Stenman, C.
    et al.
    Glavas, R.
    Davidsson, J.
    Knutsson, A.
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University.
    Visualization of liver lesions in standardized video-documented ultrasonography - inter-observer agreement and effect of contrast injection2015In: Medical ultrasonography, ISSN 1844-4172, E-ISSN 2066-8643, Vol. 17, no 3, p. 437-443Article in journal (Refereed)
    Abstract [en]

    The AIM of this study was to evaluate the inter-observer agreement and effect of contrast injection on the visibility of liver lesions by radiologists reviewing ultrasound examinations acquired by a radiographer using a standardized examination protocol. MATERIAL AND METHOD: A retrospective review was conducted by two radiologists, independently of each other, of 115 ultrasound examinations of the liver with standardized examination protocols between January 2008 and December 2012. All patients included in the study had undergone surgery for colorectal cancer. Patients attending the two-year follow-up were included. RESULTS: Focal findings, the most common of which were cysts, were seen in 42-43 out of the 115 patients before intravenous contrast and in 46-47 patients after intravenous contrast (p=0.012). The inter-observer agreement for focal findings was 86.1% before contrast, and 90.4% after contrast (n.s.), and the corresponding kappa values were 0.72 and 0.84, respectively. CONCLUSIONS: A good inter-observer agreement between two radiologists reviewing ultrasound examinations (standardized ultrasound cine-loop method acquired by a radiographer) after surgery for colorectal cancer was obtained. Injection of contrast medium increased the visibility of liver lesions.

  • 57.
    Wan, Fengkai
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Novamia AB, Uppsala, Sweden.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Novamia AB, Uppsala, Sweden.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Novamia AB, Uppsala, Sweden.
    Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution2019In: Medical Imaging 2019: Image Processing, SPIE - International Society for Optical Engineering, 2019, Vol. 10949, article id 1094909Conference paper (Refereed)
    Abstract [en]

    Intensity inhomogeneity is a great challenge for automated organ segmentation in magnetic resonance (MR) images. Many segmentation methods fail to deliver satisfactory results when the images are corrupted by a bias field. Although inhomogeneity correction methods exist, they often fail to remove the bias field completely in knee MR images. We present a new iterative approach that simultaneously predicts the segmentation mask of knee structures using a 3D U-net and estimates the bias field in 3D MR knee images using partial convolution operations. First, the test images run through a trained 3D U-net to generate a preliminary segmentation result, which is then fed to the partial convolution filter to create a preliminary estimation of the bias field using the segmented bone mask. Finally, the estimated bias field is then used to produce bias field corrected images as the new inputs to the 3D U-net. Through this loop, the segmentation results and bias field correction are iteratively improved. The proposed method was evaluated on 20 proton-density (PD)-weighted knee MRI scans with manually created segmentation ground truth using 10 fold cross-validation. In our preliminary experiments, the proposed methods outperformed conventional inhomogeneity-correction-plus-segmentation setup in terms of both segmentation accuracy and speed.

  • 58.
    Wang, Chunliang
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Connolly, Bryan
    de Oliveira Lopes, Pedro Filipe
    Frangi, Alejandro F.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Pelvis segmentation using multi-pass U-Net and iterative shape estimation2018In: Computational Methods and Clinical Applications in Musculoskeletal Imaging, Springer, 2018, Vol. 11404, p. 49-57Conference paper (Refereed)
    Abstract [en]

    In this report, an automatic method for segmentation of the pelvis in three-dimensional (3D) computed tomography (CT) images is proposed. The method is based on a 3D U-net which has as input the 3D CT image and estimated volumetric shape models of the targeted structures and which returns the probability maps of each structure. During training, the 3D U-net is initially trained using blank shape context inputs to generate the segmentation masks, i.e. relying only on the image channel of the input. The preliminary segmentation results are used to estimate a new shape model, which is then fed to the same network again, with the input images. With the additional shape context information, the U-net is trained again to generate better segmentation results. During the testing phase, the input image is fed through the same 3D U-net multiple times, first with blank shape context channels and then with iteratively re-estimated shape models. Preliminary results show that the proposed multi-pass U-net with iterative shape estimation outperforms both 2D and 3D conventional U-nets without the shape model.

  • 59.
    Wang, Chunliang
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University.
    Dahlström, Nils
    Fransson, Sven-Göran
    Lundström, Claes
    Smedby, Örjan
    KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization. Linköping University, Sweden.
    Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm2015In: Journal of Medical Imaging and Health Informatics, ISSN 2156-7018, E-ISSN 2156-7026, Vol. 5, no 8, p. 1998-2002Article in journal (Refereed)
    Abstract [en]

    A new level-set based interactive segmentation framework is introduced, where the algorithm learns the intensity distributions of the tumor and surrounding tissue from a line segment drawn by the user from the middle of the lesion towards the border. This information is used to design a likelihood function, which is then incorporated into the level-set framework as an external speed function guiding the segmentation. The endpoint of the input line segment sets a limit to the propagation of 3D region, i.e., when the zero-level-set crosses this point, the propagation is forced to stop. Finally, a fast level set algorithm with coherent propagation is used to solve the level set equation in real time. This allows the user to instantly see the 3D result while adjusting the position of the line segment to tune the parameters implicitly. The “fluctuating” character of the coherent propagation also enables the contour to coherently follow the mouse cursor’s motion when the user tries to fine-tune the position of the contour on the boundary, where the learned likelihood function may not necessarily change much. Preliminary results suggest that radiologists can easily learn how to use the proposed segmentation tool and perform relatively accurate segmentation with much less time than the conventional slice-by-slice based manual procedure.

  • 60.
    Wang, Chunliang
    et al.
    Linköping University Hospital, Sweden.
    Frimmel, Hans
    Persson, Anders
    Smedby, Örjan
    Linköping University Hospital, Sweden.
    An interactive software module for visualizing coronary arteries in CT angiography2008In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 3, no 1-2, p. 11-18Article in journal (Refereed)
    Abstract [en]

    Object: A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT). Materials and Methods: The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized "virtual contrast injection" algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3-10.5 mm. Results: The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches. Conclusion: The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.

  • 61.
    Wang, Chunliang
    et al.
    Linköping University, Sweden.
    Frimmel, Hans
    Smedby, Örjan
    Linköping University, Sweden.
    Fast level-set based image segmentation using coherent propagation2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 7, article id 073501Article in journal (Refereed)
    Abstract [en]

    Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.

  • 62.
    Wang, Chunliang
    et al.
    Linköping University, Sweden.
    Frimmel, Hans
    Smedby, Örjan
    Linköping University, Sweden.
    Level set based vessel segmentation accelerated with periodic monotonic speed function2011In: MEDICAL IMAGING 2011:: IMAGE PROCESSING, SPIE - International Society for Optical Engineering, 2011, Vol. 7962, article id UNSP 79621MConference paper (Refereed)
    Abstract [en]

    To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour's local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points' expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.

  • 63.
    Wang, Chunliang
    et al.
    Center for Medical Imaging Science and Visualization (CMIV),Department of Medical and Health Sciences (IMH).
    Moreno, Rodrigo
    Smedby, Örjan
    Linkönping University, Campus US.
    Vessel segmentation using implicit model-guided level sets2012In: : a MICCAI segmentation Challenge", Nice France, 1st of October 2012., 2012Conference paper (Refereed)
  • 64.
    Wang, Chunliang
    et al.
    Linköping University, Sweden.
    Persson, Anders
    Engvall, Jan
    De Geer, Jakob
    Czekierda, Waldemar
    Björkholm, Anders
    Fransson, Sven-Göran
    Smedby, Örjan
    Linköping University, Sweden.
    Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?2012In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 8, p. 845-851Article in journal (Refereed)
    Abstract [en]

    Background: Thanks to the development of computed tomography (CT) scanners and computer software, accurate coronary artery segmentation can be achieved with minimum user interaction. However, the question remains whether we can use these segmented images for reliable diagnosis. Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenosis. Material and Methods: CCTA data-sets from 30 patients were acquired with a 64-slice CT scanner and segmented using the region growing (RG) method and the "virtual contrast injection" (VC) method. Three types of images of each patient were reviewed by different reviewers for the presence of stenosis with diameter reduction of 50% or more. The evaluation was performed on four main arteries of each patient (120 arteries in total). For the original series, the reviewer was allowed to use all the 2D and 3D visualization tools available (conventional method). For the segmented results from RG and VC, only maximum intensity projection was used. Evaluation results were compared with catheter angiography (CA) for each artery in a blinded fashion. Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value for detecting stenosis were, respectively, 86%, 74%, and 93% for the conventional method, 83%, 71%, and 92% for VC, and 64%, 56%, and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (P < 0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (P = 0.22). Conclusion: The diagnostic accuracy for the RG-segmented 3D data is lower than those with access to 2D images, whereas the VC method shows diagnostic accuracy similar to the conventional method.

  • 65.
    Wang, Chunliang
    et al.
    Linköping University, Sweden.
    Ritter, Felix
    Smedby, Örjan
    Linköping University, Sweden.
    Making the PACS workstation a browser of image processing software: a feasibility study using inter-process communication techniques2010In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 5, no 4, p. 411-419Article in journal (Refereed)
    Abstract [en]

    Purpose To enhance the functional expandability of a picture archiving and communication systems (PACS) workstation and to facilitate the integration of third-part image-processing modules, we propose a browser-server style method. Methods In the proposed solution, the PACS workstation shows the front-end user interface defined in an XML file while the image processing software is running in the background as a server. Inter-process communication (IPC) techniques allow an efficient exchange of image data, parameters, and user input between the PACS workstation and stand-alone image-processing software. Using a predefined communication protocol, the PACS workstation developer or image processing software developer does not need detailed information about the other system, but will still be able to achieve seamless integration between the two systems and the IPC procedure is totally transparent to the final user. Results A browser-server style solution was built between OsiriX (PACS workstation software) and MeVisLab (Image-Processing Software). Ten example image-processing modules were easily added to OsiriX by converting existing MeVisLab image processing networks. Image data transfer using shared memory added <10 ms of processing time while the other IPC methods cost 1-5 s in our experiments. Conclusion The browser-server style communication based on IPC techniques is an appealing method that allows PACS workstation developers and image processing software developers to cooperate while focusing on different interests.

  • 66.
    Wang, Chunliang
    et al.
    Dept. of Radiology (IMH) and Center for Medical Image Science and Visualization (CMIV), Linköping University.
    Smedby, Örjan
    Dept. of Radiology (IMH) and Center for Medical Image Science and Visualization (CMIV), Linköping University.
    An automatic seeding method for coronary artery segmentation and skeletonization in CTA2008In: MIDAS Journal, Vol. 43, p. 1-8Article in journal (Refereed)
    Abstract [en]

    An automatic seeding method for coronary artery segmentation and skeletonization is presented. The new method includes automatic removal of the rib cage, tracing of the ascending aorta and initial planting of seeds for the coronary arteries. The automatic seeds are then passed on to a �virtual contrast injection� algorithm performing segmentation and skeletonization. In preliminary experiments, most main branches of the coronary tree were segmented and skeletonized without any user interaction.

  • 67.
    Wang, Chunliang
    et al.
    Linköping University, Linköping, Sweden.
    Smedby, Örjan
    Linköping University, Linköping, Sweden.
    Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors2014In: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), IEEE Computer Society, 2014, Vol. 6977285, p. 3327-3332Conference paper (Refereed)
    Abstract [en]

    An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging; the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.

  • 68.
    Wang, Chunliang
    et al.
    Center for Medical Imaging Science and Visualization(CMIV), Linköping University, Linköping, Sweden.
    Smedby, Örjan
    Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden.
    Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors2014In: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Challenge, co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2014), Beijing, China, May 1, 2014, CEUR-WS , 2014, Vol. 1194, p. 25-31Conference paper (Refereed)
    Abstract [en]

    An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented first, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coeffcient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.

  • 69.
    Wang, Chunliang
    et al.
    KTH, School of Technology and Health (STH).
    Smedby, Örjan
    KTH, School of Technology and Health (STH).
    Automatic whole heart segmentation using deep learning and shape context2018In: 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Springer, 2018, Vol. 10663, p. 242-249Conference paper (Refereed)
    Abstract [en]

    To assist 3D cardiac image analysis, we propose an automatic whole heart segmentation using a deep learning framework combined with shape context information that is encoded in volumetric shape models. The proposed processing pipeline consists of three major steps: scout segmentation with orthogonal 2D U-nets, shape context estimation and refining segmentation with U-net and shape context. The proposed method was evaluated using the MMWHS challenge data. Two sets of networks were trained separately for contrast-enhanced CT and MRI. On the 20 training datasets, using 5-fold cross-validation, the average Dice coefficients for the left ventricle, the right ventricle, the left atrium, the right atrium and the myocardium of the left ventricle were 0.895, 0.795, 0.847, 0.821, 0.807 for MRI and 0.935, 0.825, 0.908, 0.881, 0.879 for CT, respectively. Further improvement may be possible given more training data or advanced data augmentation strategy.

  • 70.
    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.

  • 71.
    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.

  • 72.
    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.

  • 73.
    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.

  • 74. 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/).

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