Journal article
RFPNet: Reorganizing feature pyramid networks for medical image segmentation
Computers in biology and medicine, Vol.163, 107108
09/2023
DOI: 10.1016/j.compbiomed.2023.107108
Abstract
Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.
•This segmentation method is based on CNN and designed for clinical diagnosis.•An FPR module refines semantic features at various levels with detailed information.•An SSFI module integrates features and recalibrates weights between features.•This method performs better on 4 clinical datasets than standard methods.
Details
- Title: Subtitle
- RFPNet: Reorganizing feature pyramid networks for medical image segmentation
- Creators
- Zhendong Wang - Shandong UniversityJiehua Zhu - Georgia Southern UniversityShujun Fu - Shandong UniversityShuwei Mao - Shandong UniversityYangbo Ye - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Computers in biology and medicine, Vol.163, 107108
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.compbiomed.2023.107108
- ISSN
- 0010-4825
- eISSN
- 1879-0534
- Language
- English
- Date published
- 09/2023
- Academic Unit
- Mathematics
- Record Identifier
- 9984433851502771
Metrics
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