Improved lung segmentation based on U-Net architecture and morphological operations
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Abstract (arXiv)
An essential stage in computer aided diagnosis of chest X rays is automated lung segmentation. Due to rib cages and the unique modalities of each persons lungs, it is essential to construct an effective automated lung segmentation model. This paper presents a reliable model for the segmentation of lungs in chest radiographs. Our model overcomes the challenges by learning to ignore unimportant areas in the source Chest Radiograph and emphasize important features for lung segmentation. We evaluate our model on public datasets, Montgomery and Shenzhen. The proposed model has a DICE coefficient of 98.1 percent which demonstrates the reliability of our model.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 19 Oct 2022.
Builds on
5 of 15 references.
References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
- 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
- 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
- 2020DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentationcited by 4
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Citing papers per year: 2022: 1.
1 paper.