MSENet: A multi-scale enhanced network based on unique features guidance for medical image fusion

Li, Weisheng; Li, Ruyue; Fu, Jun; Peng, Xiuxiu

doi:10.1016/j.bspc.2022.103534

Cited by 17 publications

(2 citation statements)

References 38 publications

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“…However, a single-plane fusion network model tends to ignore multiscale information from original images, resulting in an inadequate representation of fused image details. Li et al [ 32 ] introduced a multiscale enhancement fusion network (MSENet) based on unique feature guidance, utilizing a dense three-path dilated network to enlarge the receptive field for the extraction of multiscale features. Song et al [ 33 ] proposed a multiscale DenseNet (MSDNet), employing three filters of different sizes to extract multiscale features.…”

Section: Related Workmentioning

confidence: 99%

MR–CT image fusion method of intracranial tumors based on Res2Net

Chen,

Li,

Zhang

et al. 2024

BMC Med Imaging

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Background Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. Purpose The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. Methods This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. Results Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. Conclusion The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

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Section: Related Workmentioning

confidence: 99%

MR–CT image fusion method of intracranial tumors based on Res2Net

Chen,

Li,

Zhang

et al. 2024

BMC Med Imaging

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show abstract

“…However, these models are all single-plane fusion networks that largely disregard multi-scale information. Li et al [32] proposed a multi-scale enhancement network (MSENet) that uses three dilated networks to extract multi-scale features. Song, Wu, and Li [33] proposed a multi-scale dense network (MSDNet) that utilizes different convolution kernels to extract multi-scale features.…”

Section: Introductionmentioning

confidence: 99%

CFIFusion: Dual‐Branch Complementary Feature Injection Network for Medical Image Fusion

Xie,

Yu,

Ding

2024

Int J Imaging Syst Tech

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The goal of fusing medical images is to integrate the diverse information that multimodal medical images hold. However, the challenges lie in the limitations of imaging sensors and the issue of incomplete modal information retention, which make it difficult to produce images encompassing both functional and anatomical information. To overcome these obstacles, several medical image fusion techniques based on CNN or transformer architectures have been presented. Nevertheless, CNN technique struggles to establish extensive dependencies between the fused and source images, and transformer architecture often overlooks shallow complementary features. To augment both the feature extraction capacity and the stability of the model, we introduce a framework, called dual‐branch complementary feature injection fusion (CFIFusion) technique, a for multimodal medical image fusion framework that combines unsupervised models of CNN model and transformer techniques. Specifically, in our framework, the entire source image and segmented source image are input into an adaptive backbone network to learn global and local features, respectively. To further retain the source images' complementary information, we design a multi‐scale complementary feature extraction framework as an auxiliary module, focusing on calculating feature differences at each level to capture the shallow complementary information. Then, we design a shallow information preservation module tailored for sliced image characteristics. Experimental results on the Harvard whole brain atlas dataset demonstrate that CFIFusion shows greater benefits than recent state‐of‐the‐art algorithms in terms of both subjective and objective evaluations.

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Medical image fusion based on transfer learning techniques and coupled neural P systems

Dinh,

Giang

2023

Neural Comput & Applic

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MSENet: A multi-scale enhanced network based on unique features guidance for medical image fusion

Cited by 17 publications

References 38 publications

MR–CT image fusion method of intracranial tumors based on Res2Net

MR–CT image fusion method of intracranial tumors based on Res2Net

CFIFusion: Dual‐Branch Complementary Feature Injection Network for Medical Image Fusion

Medical image fusion based on transfer learning techniques and coupled neural P systems

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