Abstract
Intracranial aneurysm rupture can cause a serious stroke, which is related to the decline of daily life ability of the elderly. Although deep learning is now the most successful solution for organ detection, it requires myriads of training data, consistent of the image format, and a balanced sample distribution. This work presents an innovative representation of intracranial aneurysm detection as a shape analysis problem rather than a computer vision problem. We detected intracranial aneurysms in 3D cerebrovascular mesh models after segmentation of the brain vessels from the medical images, which can overcome the barriers of data format and data distribution, serving both clinical and screening purposes. Additionally, we propose a transferable multi-model ensemble (MMEN) architecture to detect intracranial aneurysms from cerebrovascular mesh models with limited data. To obtain a well-defined convolution operator, we use a global seamless parameterization converting a 3D cerebrovascular mesh model to a planar flat-torus. In the architecture, we transfer the planar flat-torus presentation abilities of three GoogleNet Inception V3 models, which were pre-trained on the ImageNet database, to characterize the intracranial aneurysms with local and global geometric features such as Gaussian curvature (GC), shape diameter function (SDF) and wave kernel signature (WKS), respectively. We jointly utilize all three models to detect aneurysms with adaptive weights learning based on back propagation. The experimental results on the 121 models show that our proposed method can achieve detection accuracy of 95.1% with 94.7% F1-score and 94.8% sensitivity, which is as good as the state-of-art work but is applicable to inhomogeneous image modalities and smaller datasets.
X. Wang and Z. Wu—Equally contributed to this work.
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Acknowledgement
The authors want to thank the anonymous reviewers for their constructive comments. This research was partially supported by the National Key Cooperation between the BRICS of China (No. 2017YFE0100500), National Key R&D Program of China (No. 2017YFB1002604, No. 2017YFB1402105) and Beijing Natural Science Foundation of China (No. 4172033). AFF is supported by the Royal Academy of Engineering Chair in Emerging Technologies Scheme (CiET1819\(\backslash \)19), and the OCEAN project (EP/M006328/1) and the MedIAN Network (EP/N026993/1) both funded by the Engineering and Physical Sciences Research Council (EPSRC).
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Zhou, M., Wang, X., Wu, Z., Pozo, J.M., Frangi, A.F. (2019). Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep Learning. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_27
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DOI: https://doi.org/10.1007/978-3-030-32251-9_27
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