RFPNet: Reorganizing feature pyramid networks for medical image segmentation

Wang, Zhendong; Zhu, Jiehua; Fu, Shujun; Mao, Shuwei; Ye, Yangbo

doi:10.1016/j.compbiomed.2023.107108

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Inspired by the Feature Pyramid Networks (FPNs) [22][23][24], this study introduces a multi-scale approach by reusing feature maps during the feature extraction phase. This method effectively integrates deep-level features with shallow-level features.…”

Section: Edge Contour-guided Feature Fusion Residual Networkmentioning

confidence: 99%

Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

He,

Wang,

Liu

et al. 2024

Applied Sciences

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The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.

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Section: Edge Contour-guided Feature Fusion Residual Networkmentioning

confidence: 99%