MdcFormer: transformers based on dynamic weights and multi-scale for medical image segmentation

Chenyang Ma; Xiaoru Wang; Bowen Deng

doi:10.1117/12.3033531

13 June 2024 MdcFormer: transformers based on dynamic weights and multi-scale for medical image segmentation

Chenyang Ma, Xiaoru Wang, Bowen Deng

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801G (2024) https://doi.org/10.1117/12.3033531
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Medical images often consist of multiple modalities, such as multimodal MRI images commonly used in diagnosing and studying brain tumors., and multimodal images provide rich complementary information. In the past, multimodal image segmentation usually directly added or connected modal features in the early or middle stage, which made it difficult to obtain the connection between modal features. In addition, there is a difference in information between modals and modals, and the previous method did not dealign modal features, which is likely to lead to reduced the effect of modal fusion. Thus, we propose a Multiscale dual dynamic feature fusion transformer (MdcFormer) model to explore the effects of multi-scale features, spatial and channel dynamic fusion and modal feature alignment on the segmentation effect of multimodal medical images. Utilizing a multi-encoder configuration and a single decoder, we gather characteristics from various modes at various levels and blend them in a dynamic manner across both spatial and channel domains. The proposed approach was evaluated using the BraTS2020 benchmark dataset. Empirical findings indicate that the model enhances the precision of segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyang Ma, Xiaoru Wang, and Bowen Deng "MdcFormer: transformers based on dynamic weights and multi-scale for medical image segmentation", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801G (13 June 2024); https://doi.org/10.1117/12.3033531

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