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.
Optical coherence tomography (OCT) is a non-invasive imaging modality that suitable for accessing retinal diseases. Since the thickness and shape of the retinal layer are diagnostic indicators for many ophthalmic diseases, segmentation of the retinal layer in OCT images is a critical step. Automated segmentation of oct images has made many efforts but there are still some challenges, such as lack of context information, ambiguous boundaries and inconsistent prediction of retinal lesion regions. In this work, we propose a new framework of Densely Encoded Attention Networks (DEAN) that combines dense encoders with position attention in an U-architecture for retinal layers segmentation. Since the spatial position of each layer in OCT image is relatively fixed, we use convolution in dense connections to obtain diverse feature maps in the encoder and employ position attention to improve the spatial information of learning targets. Moreover, up-sampling and skip connections in the decoder are to restore resolution by the position index saved during down-sampling, while supplementing the corresponding pixels is to guide the network capturing the global context information. This method is evaluated on two public datasets, and the results demonstrate that our method is an effective strategy on improving the performance of segmenting the retinal layers.
To deal with multitask segmentation, detection and classification of colon polyps, and solve the clinical problems of small polyps with similar background, missed detection and difficult classification, we have realized the method of supporting the early diagnosis and correct treatment of gastrointestinal endoscopy on the computer. We apply the residual U-structure network with image processing to segment polyps, and a dynamic attention deconvolutional single shot detector(DAD-SSD) to classify various polyps on colonic narrow-band images. The residual U-structure network is a two-level nested U-structure that is able to capture more contextual information, and the image processing improves the segmentation problem. DAD-SSD consists of attention deconvolutional module(ADM) and dynamic convolutional prediction module(DCPM) to extract and fuse context features. We evaluated narrow-band images, and the experimental results validate the effectiveness of the method in dealing with such multi-task detection and classification. Particularly, the mean average precision(mAP) and accuracy are superior to other methods in our experiment, which are 76.55% and 74.4% respectively.
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