Mr. Bowen Deng Profile

Bowen Deng

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Proceedings Article | 13 June 2024 Paper

MdcFormer: transformers based on dynamic weights and multiscale for medical image segmentation

Chenyang Ma, Xiaoru Wang, Bowen Deng

Proceedings Volume 13180, 131801G (2024) https://doi.org/10.1117/12.3033531

KEYWORDS: Feature fusion, Feature extraction, Image segmentation, Medical imaging, Transformers, Tumors, Convolution, Magnetic resonance imaging, Education and training, Brain

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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.

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