سال انتشار:

2020

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عنوان انگلیسی مقاله:

Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation

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ترجمه فارسی عنوان مقاله:

اعتبار سنجی الگوریتم تقسیم کبدی کاملاً خودکار با استفاده از یادگیری تقویتی عمیق چند مقیاس و مقایسه در مقابل تقسیم بندی دستی

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منبع:

Sciencedirect - Elsevier - European Journal of Radiology, 126 (2020) 108918. doi:10.1016/j.ejrad.2020.108918

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نویسنده:

David J. Winkela,b,*, Thomas J. Weikerta, Hanns-Christian Breita, Guillaume Chabinb, Eli Gibsonb, Tobias J. Heyea, Dorin Comaniciub, Daniel T. Bolla

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چکیده انگلیسی:

Purpose: To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation. Materials and methods: We retrospectively obtained 462 multiphasic CT datasets with six series for each patient: three different contrast phases and two slice thickness reconstructions (1.5/5 mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth. Results: The mean absolute error of the automatically-derived measurement was 44.3 mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p=0.697), nor on the slice thickness (p=0.446). The mean processing time/dataset with the algorithm was 9.94 s (sec) compared to manual segmentation with 219.34 s. We found an excellent agreement between both approaches with an ICC value of 0.996. Conclusion: The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation.
Keywords: Artificial intelligence | Algorithms | Reproducibility of results | Tomography | X-ray computed | Liver

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قیمت: رایگان

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توضیحات اضافی:

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