CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation

By Shuanglang Feng, Heming Zhao, Fei Shi, Xuena Cheng, Meng Wang, Yuhui Ma, Dehui Xiang, Weifang Zhu, Xinjian Chen. IEEE Transactions on Medical Imaging, 2020

594 citations recorded by the source corpus.

Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U-shape structure, we first design multiple global pyramid guidance (GPG) modules between the encoder and the decoder, aiming at providing different levels of global context information for the decoder by reconstructing skip-connection. We further design a scale-aware pyramid fusion (SAPF) module to dynamically fuse multi-scale context information in high-level features. These two pyramidal modules can exploit and fuse rich context information progressively. Experimental results show that our proposed method is very competitive with other state-of-the-art methods on four different challenging tasks, including skin lesion segmentation, retinal linear lesion segmentation, multi-class segmentation of thoracic organs at risk and multi-class segmentation of retinal edema lesions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/937253956dc6ab942764c13ab1a132238740d5b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Radu Marculescu
2. Md Mostafijur Rahman
3. Nana He
4. Ying Fu
5. Wenting Duan
6. Qing Xu
7. Xueguang Yuan
8. Dandan Li

In Computer Vision and Pattern Recognition

1. Md Mostafijur Rahman
2. Radu Marculescu
3. Nana He
4. Ying Fu
5. Wenting Duan
6. Qing Xu
7. Xueguang Yuan
8. Dandan Li

In Radiology, Nuclear Medicine and Imaging

1. Radu Marculescu
2. Ying Fu
3. Xueguang Yuan
4. Dandan Li
5. Xiaohong Huang
6. Md Mostafijur Rahman
7. Dong Liu
8. Kaiwen Zhang

Citations in the corpus

• DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation (2022, background): “Other situations could have negative influences too, including uneven illumination, ORCID (s): 0000-0001-6898-0269 (Q. Xu) low image contrast between foregroundand background,and complex tissue background [10].”
• Medical Image Segmentation via Cascaded Attention Decoding (2023, methodology): “The pyramid pooling and dilated convolution [5] are also used for lesion and organ segmentation [12, 11].”
• MISSFormer: An Effective Medical Image Segmentation Transformer (2021, background): “Gu et al. 2019; Feng et al. 2020).”
• MISSFormer: An Effective Transformer for 2D Medical Image Segmentation (2022, methodology): “To overcome the limitation, a type of method uses dilated convolution [9], [10] and pyramid pooling [11] to enlarge the receptive field as much as possible, and another type [12], [13] tries to employ few self-attention layers in high-level semantic feature maps, due to the quadratic relationship…”