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Proceedings Paper

Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method
Author(s): Duo Wang; Rui Zhang; Jin Zhu; Zhongzhao Teng; Yuan Huang; Filippo Spiga; Michael Hong-Fei Du; Jonathan H. Gillard; Qingsheng Lu; Pietro Liò
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Paper Abstract

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation.

As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image.

Paper Details

Date Published: 2 March 2018
PDF: 8 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057424 (2 March 2018); doi: 10.1117/12.2293371
Show Author Affiliations
Duo Wang, Univ. of Cambridge (United Kingdom)
Rui Zhang, Univ. of Cambridge (United Kingdom)
Jin Zhu, Univ. of Cambridge (United Kingdom)
Zhongzhao Teng, Univ. of Cambridge (United Kingdom)
Yuan Huang, Univ. of Cambridge (United Kingdom)
Filippo Spiga, Univ. of Cambridge (United Kingdom)
Michael Hong-Fei Du, Imperial College London (United Kingdom)
Jonathan H. Gillard, Univ. of Cambridge (United Kingdom)
Qingsheng Lu, Changhai Hospital (China)
Pietro Liò, Univ. of Cambridge (United Kingdom)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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