Automatic pulmonary organ and small nodule segmentation in CT scans: basing on k-means and DU-Net++.
International Conference on Bioinformatics and Intelligent Computing (BIC)(2022)
Abstract
Accurate pulmonary nodule segmentation in Computer Tomography (CT) scans is significant in clinical treatment of lung cancer. In this paper, an approach based on k-means clustering is proposed for lung organ segmentation in order to remove irrelevant chest tissues. Then a convolution neural network (CNN) model, Dense U-Net++ (DU-Net++) is constructed for detecting and segmenting nodules. The model includes three parts: down-sampling by DenseNet201 for feature extraction, up-sampling by trainable deconvolution for image restoration and middle layers by skip connection for feature fusion. The public dataset, LIDC-IDRI, is used for training and testing and the performance of DU-Net++ achieves 96.0% and 91.3% in Dice coefficient on the training set and validation set. The Dice coefficient on small nodules in validation set is 87.56%. The results indicate that the proposed model can offer a correct segmentation reference to doctors, reducing the time as well as the pressure of reading CT scans.
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