2 March 2018 Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method
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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.
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Duo Wang, Duo Wang, Rui Zhang, Rui Zhang, Jin Zhu, Jin Zhu, Zhongzhao Teng, Zhongzhao Teng, Yuan Huang, Yuan Huang, Filippo Spiga, Filippo Spiga, Michael Hong-Fei Du, Michael Hong-Fei Du, Jonathan H. Gillard, Jonathan H. Gillard, Qingsheng Lu, Qingsheng Lu, Pietro Liò, Pietro Liò, } "Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057424 (2 March 2018); doi: 10.1117/12.2293371; https://doi.org/10.1117/12.2293371
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