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Quantitative imaging biomarkers identification has become a powerful tool for predictive diagnosis given increasingly available clinical imaging data. In parallel, molecular profiles have been well documented in non-small cell lung cancers (NSCLCs). However, there has been limited studies on leveraging the two major sources for improving lung cancer computer-aided diagnosis. In this paper, we investigate...
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative...
Segmentectomy using indocyanine green (ICG) has become a primary treatment option to achieve a complete resection and preserve lung function in early-stage lung cancer. However, owing to a lack of appropriate intraoperative imaging systems, it is a huge challenge for surgeons to identify the intersegmental plane during the operation, leading to poor prognosis. Thus, we developed a novel wireless wearable...
The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic...
In order to evaluate and improve multimodal molecular imaging technology, a three-dimensional anatomical model of a BALB/c mouse was developed based on micro-CT imaging with a liver-specific contrast agent Fenestra LC. By using image processing and interactive segmentation methods, we delineated some primary organs and tissues, including the skin, skeleton, muscle, heart, lung, liver and spleen. Finally,...
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