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- W4362603641 abstract "Accurate pulmonary nodule segmentation in computed tomography (CT) images is of great importance for early diagnosis and analysis of lung diseases. Although deep convolutional networks driven medical image analysis methods have been reported for this segmentation task, it is still a challenge to precisely extract them from CT images due to various types and shapes of lung nodules. This work proposes an effective and efficient deep learning framework called enhanced square U-Net (ESUN) for accurate pulmonary nodule segmentation. We trained and tested our proposed method on publicly available data LUNA16. The experimental results showing that our proposed method can achieve Dice coefficient of 0.6896 better than other approaches with high computational efficiency, as well as reduce the network parameters significantly from 44.09M to 7.36M." @default.
- W4362603641 created "2023-04-06" @default.
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- W4362603641 date "2023-04-03" @default.
- W4362603641 modified "2023-10-16" @default.
- W4362603641 title "Pulmonary CT nodules segmentation using an enhanced square U-Net with depthwise separable convolution" @default.
- W4362603641 doi "https://doi.org/10.1117/12.2654272" @default.
- W4362603641 hasPublicationYear "2023" @default.
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