MidFusNet: Mid-Dense Fusion Network for Multi-Modal Brain MRI Segmentation

Duan, Wenting, Zhang, Lei, Colman, Jordan , Gulli, Giosue and Ye, Xujiong (2023) MidFusNet: Mid-Dense Fusion Network for Multi-Modal Brain MRI Segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022.

Full content URL: https://doi.org/10.1007/978-3-031-33842-7_9

MidFusNet: Mid-Dense Fusion Network for Multi-Modal Brain MRI Segmentation
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Item Type:Conference or Workshop contribution (Paper)
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The fusion of multi-modality information has proved effective at improving the segmentation results of targeted regions (e.g., tumours, lesions or organs) of medical images. In particular, layer-level fusion represented by DenseNet has demonstrated a promising level of performance for various medical segmentation tasks. Using stroke and infant brain segmentation as example of ongoing challenging applications involving multi-modal images, we investigate whether it is possible to create a more effective of parsimonious fusion architecture based on the state-of-art fusion network - HyperDenseNet. Our hypothesis is that by fully fusing features throughout the entire network from different modalities, this not only increases network computation complexity but also interferes with the unique feature learning of each modality. Nine new network variants involving different fusion points and mechanisms are proposed. Their performances are evaluated on public datasets including iSeg-2017 and ISLES15-SSIS and an acute stroke lesion dataset collected by medical professionals. The experiment results show that of the nine proposed variants, the ‘mid-dense’ fusion network (named as MidFusNet) is able to achieve a performance comparable to the state-of-art fusion architecture, but with a much more parsimonious network (i.e., ~3.5 million parameters less compared to the baseline network for three modalities).

Keywords:Multi-Modal Fusion, Dense Network, Brain Segmentation
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:52259
Deposited On:07 Nov 2022 13:56

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