Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network

Duan, Wenting, Zhang, Lei, Colman, Jordan , Gulli, Giosue and Ye, Xujiong (2021) Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network. In: The 4th International Workshop on Machine Learning in Clinical Neuroimaging, 2021 MICCAI Workshop, September 27, 2021, Strasbourg, France.

Full content URL: https://doi.org/10.1007/978-3-030-87586-2_9

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Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network
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Abstract

Algorithms for fusing information acquired from different imaging modalities have shown to improve the segmentation results of various applications in the medical field. Motivated by recent successes achieved using densely connected fusion networks, we propose a new fusion architecture for the purpose of 3D segmentation in multi-modal brain MRI volumes. Based on a hyper-densely connected convolutional neural network, our network features in promoting a progressive information abstraction process, introducing a new module – ResFuse to merge and normalize features from different modalities and adopting combo loss for handing data imbalances. The proposed approach is evaluated on both an outsourced dataset for acute ischemic stroke lesion segmentation and a public dataset for infant brain segmentation (iSeg-17). The experiment results show our approach achieves superior performances for both
datasets compared to the state-of-art fusion network.

Keywords:Multi-Modal Fusion, Dense Network, Brain Segmentation
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:46688
Deposited On:06 Oct 2021 14:00

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