Convolutional Neural Network Ensemble Segmentation With Ratio-Based Sampling for the Arteries and Veins in Abdominal CT Scans

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 5; pp. 1518 - 1526
• Main Authors: Golla, Alena-Kathrin, Bauer, Dominik F., Schmidt, Ralf, Russ, Tom, Norenberg, Dominik, Chung, Khanlian, Tonnes, Christian, Schad, Lothar R., Zollner, Frank G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abdomen; Annotations; Arteries; Artificial neural networks; Blood vessels; Computed tomography; Datasets; Entropy; Image segmentation; Neural networks; Sampling; Sampling methods; Segmentation; Similarity coefficient method; Three-dimensional displays; Training; Two dimensional displays; Veins; Veins & arteries
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.3042640

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 IRCAD subjects. Best performing networks were combined into an ensemble. We investigated seven different weighting 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 <inline-formula><tex-math notation="LaTeX">\boldsymbol{\pm }</tex-math></inline-formula> 0.050 (veins) and 0.838 <inline-formula><tex-math notation="LaTeX">\boldsymbol{\pm }</tex-math></inline-formula> 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 segmentation pipeline can provide valuable information for the planning of living donor organ transplantations.