عنوان انگلیسی مقاله:
Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation
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
Sciencedirect - Elsevier - European Journal of Radiology, 126 (2020) 108918. doi:10.1016/j.ejrad.2020.108918
David J. Winkela,b,*, Thomas J. Weikerta, Hanns-Christian Breita, Guillaume Chabinb, Eli Gibsonb, Tobias J. Heyea, Dorin Comaniciub, Daniel T. Bolla
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