Pulmonary vessel segmentation from CT images is essential to diagnosis and treatment of lung diseases, particularly in treatment planning and clinical outcome evaluation. The main challenge for pulmonary vessel segmentation is complicated structures of the vascular trees and their similar intensity values with other tissues like the tracheal wall and lung nodules. This paper presents a novel relation extractor U-shaped network combining convolution and self-attention mechanism in an encoder-decoder mode. Particularly, we employ convolution in the shallow layers to extract local information of vessels in a short range and apply self-attention in the deep layers to capture long-range contextual relationship between ancestors and descendants of the vascular tree. We evaluate our proposed method on 50 computer tomography volumes, with the experimental results showing that our method can improve the average coefficient dice and recall to 85.60 and 86.04 respectively.
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.
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.
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