Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocar...Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocarcinoma as the most common type.In recent years,with an increasingly aging society and the growing number of obese people,the incidence of EC is constantly rising,posing a serious threat to women’s health.Some studies have reported that the interruption of digestion and absorption caused by imbalance in intestinal microbiota may lead to conditions such as obesity,hypertension,diabetes,and hormone imbalance,which are all risk factors for EC.Meanwhile,intestinal bacteria produce a series of metabolites during colonization and reproduction,which can rapidly respond to changes in the microenvironment of the body.Changes in their types and quantities can serve as sensitive indicators of physiological and pathological changes in the body.Patients with EC often suffer from metabolic diseases,which can lead to metabolic disorders involving carbohydrates,fats,and amino acid in their bodies.展开更多
Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on...Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations.展开更多
基金funded by the Fundamental Research Program of Shanxi Province(Grant number 202103021224394).
文摘Endometrial cancer(EC)is a malignant tumour that occurs in the epithelial cells of the endometrium and represents one of the most common malignancies involving the female reproductive system,with endometrioid adenocarcinoma as the most common type.In recent years,with an increasingly aging society and the growing number of obese people,the incidence of EC is constantly rising,posing a serious threat to women’s health.Some studies have reported that the interruption of digestion and absorption caused by imbalance in intestinal microbiota may lead to conditions such as obesity,hypertension,diabetes,and hormone imbalance,which are all risk factors for EC.Meanwhile,intestinal bacteria produce a series of metabolites during colonization and reproduction,which can rapidly respond to changes in the microenvironment of the body.Changes in their types and quantities can serve as sensitive indicators of physiological and pathological changes in the body.Patients with EC often suffer from metabolic diseases,which can lead to metabolic disorders involving carbohydrates,fats,and amino acid in their bodies.
基金supported by the National Natural Science Foundation of China (U21A20205)Key Projects of Natural Science Foundation of Hubei Province (2021CFA059)+1 种基金Fundamental Research Funds for the Central Universities (2021ZKPY006)cooperative funding between Huazhong Agricultural University and Shenzhen Institute of Agricultural Genomics (SZYJY2021005,SZYJY2021007)。
文摘Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations.
