The three-dimensional reconstruction of bronchopulmonary segments based on computed tomography (CT) is very critical in lesion, lung cancer localization and surgical resection. However, there is currently no fast and accurate method for three-dimensional reconstruction of pulmonary segments, and the process of labeling pulmonary segments needs to rely on other information such as bronchi and blood vessels, which will greatly consume the time and mental cost of doctors. In this paper, based on the principle of pulmonary segments division, we propose a two-stage fast pulmonary segments division method based on segmental bronchi. Specifically, for a CT image, we employ two well-trained nnUNet models in the first stage to accurately segment 5 lobes and 18 segmental bronchi, respectively. This is because each pulmonary segment should encompass its corresponding segmental bronchi, while lung lobe boundaries exhibit greater distinctiveness compared to those of pulmonary segments. In the second stage, we consider the distance from each pixel point to the segmental bronchi of various pulmonary segments in each lobe, and further divide each lobe to obtain the final 18 types of segments. Finally, we visually validated the rationality of the results by employing the principle of using pulmonary veins as demarcations for pulmonary segments.
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.
Monocular depth estimation is a popular task. Due to the difficulty of obtaining true depth labels for the bronchus and the characteristics of the bronchial image such as scarcity of texture, smoother surfaces and more holes, there are many challenges in bronchial depth estimation. Hence, we propose to use a ray tracing algorithm to generate virtual images along with their corresponding depth maps to train an asymmetric encoder-decoder transformer network for bronchial depth estimation. We propose the edge-aware unit to enhance the awareness of the bronchial internal structure considering that the bronchus has few texture features and many edges and holes. And asymmetric encoder-decoder is proposed by us for multi-layer features fusion. The experimental results of the virtual bronchial demonstrate that our method achieves the best results in several metrics, including MAE of 0.915 ± 0.596 and RMSE of 1.471 ± 1.097.
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.
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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