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  • 1
    Online Resource
    Online Resource
    One-stop computerized virtual planning system for the surgical management of posterior wall acetabular fractures
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Orthopaedic Surgery and Research Vol. 17, No. 1 ( 2022-10-04)
    Details
    In: Journal of Orthopaedic Surgery and Research, Springer Science and Business Media LLC, Vol. 17, No. 1 ( 2022-10-04)
    Abstract: Posterior wall acetabular fractures remain one of the most difficult fracture injuries to treat. Accurate assessment of fracture characteristics and appropriate preoperative surgical strategies are essential for excellent reduction. This paper evaluates the feasibility and effectiveness of a one-stop computerized virtual planning system for the surgical management of posterior wall acetabular fractures. Methods 52 cases of posterior wall acetabular fractures treated surgically were selected in our department between January 2015 and December 2020 for retrospective analysis. 52 cases were classified into group A (25 patients) and group B (27 patients) according to whether computerized virtual planning procedures were performed preoperatively. In group A, virtual surgical simulation was conducted using a one-stop computerized planning system preoperatively. In group B, traditional surgery was employed. Reduction quality, surgical time, blood loss, hip function, complications, and instrumentation time were compared between the two groups. Results The actual surgery for all patients in group A was essentially the same as the virtual surgery before the operation. Compared to group B, patients in group A had markedly shorter surgical time (−43 min), shorter instrumentation time (−20 min), and less intraoperative blood loss (−130 ml). However, no significant statistical difference was observed in reduction quality and hip function. The complication rate was slightly lower in group A (4/25) than in group B (7/27), without a significant difference. Conclusion The one-stop computerized virtual planning system is a highly effective, user-friendly and educational tool for allowing the cost-efficient surgical simulation of posterior wall acetabular fractures and providing a more individualized therapeutic schedule. The one-stop computerized planning system is feasible to treat posterior wall acetabular fractures, which is an effective method than the conventional treatment of posterior wall acetabular fractures. Trial registration : retrospective registration.
    Type of Medium: Online Resource
    ISSN: 1749-799X
    URL: Article
    DOI: 10.1186/s13018-022-03333-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2252548-8
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  • 2
    Online Resource
    Online Resource
    A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images
    Wiley ; 2022
    In:  Journal of Applied Clinical Medical Physics Vol. 23, No. 1 ( 2022-01)
    Details
    In: Journal of Applied Clinical Medical Physics, Wiley, Vol. 23, No. 1 ( 2022-01)
    Abstract: Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.
    Type of Medium: Online Resource
    ISSN: 1526-9914 , 1526-9914
    URL: Issue
    URL: Article
    DOI: 10.1002/acm2.v23.1
    DOI: 10.1002/acm2.13482
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2010347-5
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  • 3
    Online Resource
    Online Resource
    CNN-based neural network model for amplified laser pulse temporal shape prediction with dynamic requirement in high-power laser facility
    Optica Publishing Group ; 2022
    In:  Optics Express Vol. 30, No. 17 ( 2022-08-15), p. 29885-
    Details
    In: Optics Express, Optica Publishing Group, Vol. 30, No. 17 ( 2022-08-15), p. 29885-
    Abstract: The temporal shape of laser pulses is one of the essential performances in the inertial confinement fusion (ICF) facility. Due to the complexity and instability of the laser propagation system, it is hard to predict the pulse shapes precisely by pure analytic methods based on the physical model [Frantz-Nodvik (F-N) equation]. Here, we present a data-driven model based on a convolutional neural network (CNN) for precise prediction. The neural network model introduces sixteen parameters neglected in the F-N equation based models to expand the representation dimension. The sensitivity analysis of the experimental results confirms that these parameters have different degrees of influence on the temporal output shapes and cannot be ignored. The network characterizes the whole physical process with commonality and specificity features to improve the description ability. The prediction accuracy evaluated by a root mean square of the proposed model is 7.93%, which is better compared to three optimized physical models. This study explores a nonanalytic methodology of combining prior physical knowledge with data-driven models to map the complex physical process by numerical models, which has strong representation capability and great potential to model other measurable processes in physical science.
    Type of Medium: Online Resource
    ISSN: 1094-4087
    URL: Article
    DOI: 10.1364/OE.461396
    Language: English
    Publisher: Optica Publishing Group
    Publication Date: 2022
    detail.hit.zdb_id: 1491859-6
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  • 4
    Online Resource
    Online Resource
    Immobilization of Lewis Basic Sites into a Stable Ethane-Selective MOF Enabling One-Step Separation of Ethylene from a Ternary Mixture
    American Chemical Society (ACS) ; 2022
    In:  Journal of the American Chemical Society Vol. 144, No. 6 ( 2022-02-16), p. 2614-2623
    Details
    In: Journal of the American Chemical Society, American Chemical Society (ACS), Vol. 144, No. 6 ( 2022-02-16), p. 2614-2623
    Type of Medium: Online Resource
    ISSN: 0002-7863 , 1520-5126
    URL: Article
    URL: Article
    DOI: 10.1021/jacs.1c10973
    DOI: 10.1021/jacs.1c10973.s001
    RVK:
    VA 1120 ; VA 1752
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2022
    detail.hit.zdb_id: 1472210-0
    detail.hit.zdb_id: 3155-0
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  • 5
    Online Resource
    Online Resource
    Calcaneal reticular plate with cannulated screws fixation for comminuted Hoffa fracture: A case report
    Elsevier BV ; 2022
    In:  Trauma Case Reports Vol. 40 ( 2022-08), p. 100656-
    Details
    In: Trauma Case Reports, Elsevier BV, Vol. 40 ( 2022-08), p. 100656-
    Type of Medium: Online Resource
    ISSN: 2352-6440
    URL: Article
    DOI: 10.1016/j.tcr.2022.100656
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2835433-3
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  • 6
    Online Resource
    Online Resource
    Abdomen tissues segmentation from computed tomography images using deep learning and level set methods
    American Institute of Mathematical Sciences (AIMS) ; 2022
    In:  Mathematical Biosciences and Engineering Vol. 19, No. 12 ( 2022), p. 14074-14085
    Details
    In: Mathematical Biosciences and Engineering, American Institute of Mathematical Sciences (AIMS), Vol. 19, No. 12 ( 2022), p. 14074-14085
    Abstract: 〈abstract〉 〈p〉Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surrounding organs. In this paper, an attention-based deep learning method for automated abdomen tissues segmentation is proposed. In our method, image cropping is first applied to the original images. U-net model with attention mechanism is then constructed to obtain the initial abdomen tissues. Finally, level set evolution which consists of three energy terms is used for optimize the initial abdomen segmentation. The proposed model is evaluated across 470 subsets. For liver segmentation, the mean dice are 96.2 and 95.1% for the FLARE21 datasets and the LiTS datasets, respectively. For kidney segmentation, the mean dice are 96.6 and 95.7% for the FLARE21 datasets and the LiTS datasets, respectively. Experimental evaluation exhibits that the proposed method can obtain better segmentation results than other methods.〈/p〉 〈/abstract〉
    Type of Medium: Online Resource
    ISSN: 1551-0018
    URL: Article
    DOI: 10.3934/mbe.2022655
    Language: Unknown
    Publisher: American Institute of Mathematical Sciences (AIMS)
    Publication Date: 2022
    detail.hit.zdb_id: 2265126-3
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