Details

Autor(en) / Beteiligte

• Winkel, David J.
• Weikert, Thomas J.
• Breit, Hanns-Christian
• Chabin, Guillaume
• Gibson, Eli
• Heye, Tobias J.
• Comaniciu, Dorin
• Boll, Daniel T.

Titel

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

Ist Teil von

• European journal of radiology, 2020-05, Vol.126, p.108918-108918, Article 108918

Ort / Verlag

Ireland: Elsevier B.V

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• •Liver volumetric analyses contain relevant clinical information.•Various methodologies for liver volumetric analyses have been used in the past.•Deep reinforcement learning (DRL) has emerged as a promising technology.•Liver volumetric analyses using DRL show accurate, robust and fast results. 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. 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. 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. 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.