The Experts below are selected from a list of 36954 Experts worldwide ranked by ideXlab platform
Abbas Samani - One of the best experts on this subject based on the ideXlab platform.
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measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples
Physics in Medicine and Biology, 2009Co-Authors: Joseph J Ohagan, Abbas SamaniAbstract:The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include Surgery Planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C(11) showing the most significant difference. Furthermore, statistical analysis indicates that C(02) of the Yeoh model, and C(11) and C(20) of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as Surgery Planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.
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elastic moduli of normal and pathological human breast tissues an inversion technique based investigation of 169 samples
Physics in Medicine and Biology, 2007Co-Authors: Abbas Samani, Judit Zubovits, Donald B PlewesAbstract:Understanding and quantifying the mechanical properties of breast tissues has been a subject of interest for the past two decades. This has been motivated in part by interest in modelling soft tissue response for Surgery Planning and virtual-reality-based surgical training. Interpreting elastography images for diagnostic purposes also requires a sound understanding of normal and pathological tissue mechanical properties. Reliable data on tissue elastic properties are very limited and those which are available tend to be inconsistent, in part as a result of measurement methodology. We have developed specialized techniques to measure tissue elasticity of breast normal tissues and tumour specimens and applied them to 169 fresh ex vivo breast tissue samples including fat and fibroglandular tissue as well as a range of benign and malignant breast tumour types. Results show that, under small deformation conditions, the elastic modulus of normal breast fat and fibroglandular tissues are similar while fibroadenomas were approximately twice the stiffness. Fibrocystic disease and malignant tumours exhibited a 3–6-fold increased stiffness with high-grade invasive ductal carcinoma exhibiting up to a 13-fold increase in stiffness compared to fibrogalndular tissue. A statistical analysis showed that differences between the elastic modulus of the majority of those tissues were statistically significant. Implications for the specificity advantages of elastography are reviewed. For more information on this article, see medicalphysicsweb.org
Dexing Kong - One of the best experts on this subject based on the ideXlab platform.
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automatic abdominal multi organ segmentation using deep convolutional neural network and time implicit level sets
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Jialin Peng, Yuanyuan Bao, Feng Chen, Dexing KongAbstract:Purpose Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and Surgery Planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images.
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automatic 3d liver location and segmentation via convolutional neural network and graph cut
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Zhiyi Peng, Dexing KongAbstract:Purpose Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as Surgery Planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.
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automatic 3d liver segmentation based on deep learning and globally optimized surface evolution
Physics in Medicine and Biology, 2016Co-Authors: Peijun Hu, Fa Wu, Jialin Peng, Ping Liang, Dexing KongAbstract:The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver Surgery Planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of , yielding a mean Dice similarity coefficient of , and an average symmetric surface distance of mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.
Brian T Denton - One of the best experts on this subject based on the ideXlab platform.
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chance constrained Surgery Planning under conditions of limited and ambiguous data
Informs Journal on Computing, 2019Co-Authors: Yan Deng, Siqian Shen, Brian T DentonAbstract:Surgery Planning decisions include which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence, and time to start each Surgery. They are often made under uncertain Surgery duratio...
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chance constrained Surgery Planning under conditions of limited and ambiguous data
Social Science Research Network, 2016Co-Authors: Yan Deng, Siqian Shen, Brian T DentonAbstract:Surgery Planning include decisions of which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence and time to start each Surgery. The decisions are often made under uncertain Surgery durations with limited data that leads to unknown distributional information; furthermore, cost data for criteria such as overtime and Surgery delays are often difficult or impossible to estimate in practice. In this paper, we consider a distributionally robust (DR) formulation that recognizes practical limitations on data availability and obviates the need to provide accurate cost parameters for Surgery Planning. We minimize the cost of opening ORs for completing a set of surgeries, subject to a joint DR chance constraint on OR overtime. We use statistical $\phi$-divergence measures to build an ambiguity set of possible distributions of random Surgery durations, and derive a branch-and-cut algorithm for optimizing a mixed-integer linear programming reformulation of the DR chance-constrained model formulated based on a finite sample of scenarios. We compute instances generated from real hospital-based Surgery data, demonstrate the computational efficacy of our approach, and provide insights for DR Surgery Planning.
Siqian Shen - One of the best experts on this subject based on the ideXlab platform.
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Non-profit resource allocation and service scheduling with cross-subsidization and uncertain resource consumptions
Omega-international Journal of Management Science, 2020Co-Authors: Hideaki Nakao, Siqian Shen, Lin ZhaoAbstract:Abstract We consider a mixture of for-profit and non-profit requests that share multiple resources at random consumption rates. The revenue from fulfilling for-profit requests is used to cross-subsidize the cost of non-profit operations, and we aim to maximize the number of completed non-profit service requests. We consider two problems that respectively optimize resource allocation and service schedules, and employ chance constraints to restrict the probability of undesired outcomes such as resource over-utilization and service delay. For the allocation model, we propose three approximations of the chance-constrained program, and derive their variants to allow variable resource capacities. For the scheduling model, we derive a mixed-integer linear programming reformulation and develop a two-phase algorithm that separately decides allocation decisions and the start time of each service request. We conduct numerical studies on randomly generated instances of non-profit Surgery Planning to demonstrate the computational results of different models, and the impact of varying parameters and cross-subsidization on non-profit operations.
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chance constrained Surgery Planning under conditions of limited and ambiguous data
Informs Journal on Computing, 2019Co-Authors: Yan Deng, Siqian Shen, Brian T DentonAbstract:Surgery Planning decisions include which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence, and time to start each Surgery. They are often made under uncertain Surgery duratio...
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chance constrained Surgery Planning under conditions of limited and ambiguous data
Social Science Research Network, 2016Co-Authors: Yan Deng, Siqian Shen, Brian T DentonAbstract:Surgery Planning include decisions of which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence and time to start each Surgery. The decisions are often made under uncertain Surgery durations with limited data that leads to unknown distributional information; furthermore, cost data for criteria such as overtime and Surgery delays are often difficult or impossible to estimate in practice. In this paper, we consider a distributionally robust (DR) formulation that recognizes practical limitations on data availability and obviates the need to provide accurate cost parameters for Surgery Planning. We minimize the cost of opening ORs for completing a set of surgeries, subject to a joint DR chance constraint on OR overtime. We use statistical $\phi$-divergence measures to build an ambiguity set of possible distributions of random Surgery durations, and derive a branch-and-cut algorithm for optimizing a mixed-integer linear programming reformulation of the DR chance-constrained model formulated based on a finite sample of scenarios. We compute instances generated from real hospital-based Surgery data, demonstrate the computational efficacy of our approach, and provide insights for DR Surgery Planning.
Hsiu-hsia Lin - One of the best experts on this subject based on the ideXlab platform.
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outcome of facial contour asymmetry after conventional two dimensional versus computer assisted three dimensional Planning in cleft orthognathic Surgery
Scientific Reports, 2020Co-Authors: Pojung Hsu, Rafael Denadai, Betty Chienjung Pai, Hsiu-hsia LinAbstract:Computer-assisted 3D Planning has overcome the limitations of conventional 2D Planning-guided orthognathic Surgery (OGS), but difference for facial contour asymmetry outcome has not been verified to date. This comparative study assessed the facial contour asymmetry outcome of consecutive patients with unilateral cleft lip and palate who underwent 2D Planning (n = 37)- or 3D simulation (n = 38)-guided OGS treatment for correction of maxillary hypoplasia and skeletal Class III malocclusion between 2010 and 2018. Normal age-, gender-, and ethnicity-matched individuals (n = 60) were enrolled for comparative analyses. 2D (n = 60, with 30 images for each group) and 3D (n = 43, with 18 and 25 images for 2D Planning and 3D simulation groups, respectively) photogrammetric-based facial contour asymmetry-related measurements were collected from patients and normal individuals. The facial asymmetry was further verified by using subjective perception of a panel composed of 6 blinded raters. On average, the facial contour asymmetry was significantly (all p < 0.05) reduced after 3D virtual Surgery Planning for all tested parameters, with no significant differences between post-OGS 3D simulation-related values and normal individuals. No significant differences were observed for pre- and post-OGS values in conventional 2D Planning-based treatment, with significant (all p < 0.05) differences for all normal individuals-related comparisons. This study suggests that 3D Planning presents superior facial contour asymmetry outcome than 2D Planning.
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Selection of a horizontal reference plane in 3D evaluation: Identifying facial asymmetry and occlusal cant in orthognathic Surgery Planning
Scientific Reports, 2017Co-Authors: Daniel Lonic, Ali Sundoro, Hsiu-hsia Lin, Pei-ju LinAbstract:Facial asymmetry and dental occlusal cant have been detected in two-dimensional cephalometry using different horizontal reference lines, but equivalent 3-dimensional (3D) reference planes have not been thoroughly investigated. In this study, 3D cone-beam computed tomography scans of 83 consecutive patients were evaluated using a standardized 3D frame and three horizontal reference planes, Supraorbitale (Sor), Frontozygomatic (Z), and Frankfurt horizontal (FH) for cant detection. Canting was defined as a vertical difference between left and right sides of 2 mm or more, and in at least two investigated planes. Concordance for negative canting was found in 38 patients, and for positive canting in 22 patients. Discordance in cant detection was found in 23 patients (28%). 29 patients were found to have canting in at least 2 planes. The FH plane was discordant to the other two planes in 4 patients, the Sor plane in 7 patients and the Z plane in 12 patients. Youden’s index showed the highest performance for FH (0.878), followed by Sor (0.823) and Z plane (0.762). This study revealed that the FH plane was the best method for cant detection in 3D imaging. The FH plane and Sor plane can be combined if orbital asymmetry is suspected.