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.
The problems of the large variation in shape and location, and the complex background of many neighboring tissues in the pancreas segmentation hinder the early detection and diagnosis of pancreatic diseases. The U-Net family achieve great success in various medical image processing tasks such as segmentation and classification. This work aims to comparatively evaluate 2D U-Net, 2D U-Net++ and 2D U-Net3+ for CT pancreas segmentation. More interestingly, We also modify U-Net series in accordance with depth wise separable convolution (DWC) that replaces standard convolution. Without DWC, U-Net3+ works better than the other two networks and achieves an average dice similarity coefficient of 0.7555. Specifically, according to this study, we find that U-Net plus a simple module of DWC certainly works better than U-Net++ using redesigned dense skip connections and U-Net3+ using full-scale skip connections and deep supervision and can obtain an average dice similarity coefficient of 0.7613. More interestingly, the U-Net series plus DWC can significantly reduce the amount of training parameters from (39.4M, 47.2M, 27.0M) to (14.3M, 18.4M, 3.15M), respectively. At the same time, they also improve the dice similarity compared to using normal convolution.
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