An image segmentation algorithm based on multi-scale feature pyramid network
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Abstract (arXiv)
Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases. For example, in clinical cervical cancer radiotherapy, after acquiring subabdominal MRI images, a fast and accurate image segmentation of organs and tumors in MRI images can optimize the clinical radiotherapy process, whereas traditional approaches use manual annotation by specialist doctors, which is time-consuming and laborious, therefore, automatic organ segmentation of subabdominal MRI images is a valuable research topic.
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
- 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
- 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
- 2019Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Imagescited by 28
- 2018MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentationcited by 27
- 2019Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentationcited by 14
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