Convolutional Neural Network Ensemble Segmentation with Ratio-based Sampling for the Arteries and Veins in Abdominal CT Scans.

2020 
Objective Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment in various clinical scenarios. We present a fully automatic method for the extraction and differentiation of the arterial and venous vessel trees from abdominal contrast enhanced computed tomography (CE-CT) volumes using convolutional neural networks (CNNs). Methods We used a novel ratio-based sampling method to train 2D and 3D versions of the U-Net, the V-Net and the DeepVesselNet. Networks were trained with a combination of the Dice and cross entropy loss. Performance was evaluated on 20 IR-CAD subjects. Best performing networks were combined into an ensemble. We investigated seven different weight-ing schemes. Trained networks were additionally applied to 26 BTCV cases to validate the generalizability. Results Based on our experiments, the optimal configuration is an equally weighted ensemble of 2D and 3D U-and V-Nets. Our method achieved Dice similarity coefficients of 0.758 ± 0.050 (veins) and 0.838 ± 0.074 (arteries) on the IRCAD data set. Application to the BTCV data set showed a high transfer ability. Conclusion Abdominal vascular structures can be segmented more accurately using ensembles than individual CNNs. 2D and 3D networks have complementary strengths and weaknesses. Our ensemble of 2D and 3D U-Nets and V-Nets in combination with ratio-based sampling achieves a high agreement with manual annotations for both artery and vein segmentation. Our results surpass other state-of-the-art methods. Significance Our segmen-tation pipeline can provide valuable information for the planning of living donor organ transplantations.
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