Zhou, M, Wang, X, Wu, Z et al. (2 more authors) (2019) Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep Learning. In: Lecture Notes in Computer Science. MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 13-17 Oct 2019, Shenzhen, China. Springer Verlag , pp. 243-252. ISBN 9783030322502
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 May 2020 12:12 |
Last Modified: | 13 May 2020 12:12 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-32251-9_27 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160602 |