The segmentation of pulmonary arteries and veins in computed tomography scans is crucial for the diagnosis and assessment of pulmonary diseases. This paper discusses the challenges in segmenting these vascular structures, such as the classification of terminal pulmonary vessels relying on information from distant root vessels, and the complex branches and crossings of arteriovenous vessels. To address these difficulties, we introduce a fully automatic segmentation method that utilizes multiple 3D residual U-blocks module, a semantic embedding module, and a semantic perception module. The 3D residual U-blocks module can extract multi-scale features under a high receptive field, the semantic embedding module embeds semantic information to aid the network in utilizing the anatomical characteristics of parallel pulmonary artery and bronchi, and the SPM perceives semantic information and decodes it into classification results for pulmonary arteries and veins. Our approach was evaluated on a dataset of 57 lung CT scans and demonstrated competitive performance compared to existing medical image segmentation models.
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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