AIM: To investigate feasibility and accuracy of near-infrared fluorescence imaging using indocyanine green: nanocolloid for sentinel lymph node (SLN) detection in gastric cancer.METHODS: A prospective, single-institut...AIM: To investigate feasibility and accuracy of near-infrared fluorescence imaging using indocyanine green: nanocolloid for sentinel lymph node (SLN) detection in gastric cancer.METHODS: A prospective, single-institution, phase I feasibility trial was conducted. Patients suffering from gastric cancer and planned for gastrectomy were included. During surgery, a subserosal injection of 1.6 mL ICG:Nanocoll was administered around the tumor. NIR fluorescence imaging of the abdominal cavity was performed using the Mini-FLARE™ NIR fluorescence imaging system. Lymphatic pathways and SLNs were visualized. Of every detected SLN, the corresponding lymph node station, signal-to-background ratio and histopathological diagnosis was determined. Patients underwent standard-of-care gastrectomy. Detected SLNs outside the standard dissection planes were also resected and evaluated.RESULTS: Twenty-six patients were enrolled. Four patients were excluded because distant metastases were found during surgery or due to technical failure of the injection. In 21 of the remaining 22 patients, at least 1 SLN was detected by NIR Fluorescence imaging (mean 3.1 SLNs; range 1-6). In 8 of the 21 patients, tumor-positive LNs were found. Overall accuracy of the technique was 90% (70%-99%; 95%CI), which decreased by higher pT-stage (100%, 100%, 100%, 90%, 0% for respectively Tx, T1, T2, T3, T4 tumors). All NIR-negative SLNs were completely effaced by tumor. Mean fluorescence signal-to-background ratio of SLNs was 4.4 (range 1.4-19.8). In 8 of the 21 patients, SLNs outside the standard resection plane were identified, that contained malignant cells in 2 patients.CONCLUSION: This study shows successful use of ICG:Nanocoll as lymphatic tracer for SLN detection in gastric cancer. Moreover, tumor-containing LNs outside the standard dissection planes were identified.展开更多
Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(N...Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.展开更多
喀斯特石漠化是中国西南地区严重的生态环境问题。哨兵数据作为Landsat和SPOT(systeme probatoire d’observation de la terre)系列卫星数据的继承和延续,在生态环境监测中具有很好的应用前景。采用传统方式对Sentinel-2A数据进行喀斯...喀斯特石漠化是中国西南地区严重的生态环境问题。哨兵数据作为Landsat和SPOT(systeme probatoire d’observation de la terre)系列卫星数据的继承和延续,在生态环境监测中具有很好的应用前景。采用传统方式对Sentinel-2A数据进行喀斯特石漠化信息提取,构建20 m分辨率的石漠化指数,将损失Sentinel-2A数据的细节信息。为了更精确地对石漠化信息进行提取,结合Sentinel-2A数据的自身特点,构建了10 m分辨率的石漠化指数,提取了云南省文山州北门河流域的喀斯特石漠化信息并对其地质成因进行分析。通过对4种不同的融合算法进行对比可知,采用àtrous小波变换对短波红外波段(B12)进行融合的效果优于高通滤波、主成分分析和Gram-Schmidt变换等融合算法,由该方法构建的10 m归一化微分岩石指数(normalized differential rocky index, NDRI)和岩石裸露率(fr)不仅与20 m分辨率的NDRI和fr保持了较好的相关性(相关系数分别为0.90和0.81),而且能够更好地突出不同地物的特征。结果表明,构建10 m分辨率的NDRI和fr能够更好地对喀斯特石漠化信息进行提取。对北门河流域喀斯特石漠化地质成因分析的结果表明:北门河流域喀斯特石漠化以潜在石漠化和轻度石漠化类型为主,占流域总面积的40.66%、16.97%;流域喀斯特石漠化主要分布于个旧组和板纳组中;在小于25°的坡度范围内,各类喀斯特石漠化分布集中,占喀斯特石漠化总面积的82.71%。展开更多
In order to solve the problems of small monitoring range,long time and high cost of existing sedimentation observation methods,based on two-view sentinel No.1 radar images of Guqiao mining area in Huainan City from No...In order to solve the problems of small monitoring range,long time and high cost of existing sedimentation observation methods,based on two-view sentinel No.1 radar images of Guqiao mining area in Huainan City from November 4,2017 to November 28,2017,surface change information was obtained in combination with D-InSAR,and the three-dimensional surface deformation was monitored by two-pass method and single line of sight D-InSAR method.The results show that during the research period of 24 d,the maximum deformation of the mining area reached 71 mm,and the southern subsidence was the most obvious,which was in line with the mining subsidence law.The maximum displacement from the north to the south was about 250 mm,while the maximum displacement from the east to the west was about 80 mm,and the maximum subsidence in the center was 110 mm.It is concluded that D-InSAR technique has a good effect on the inversion of the mining subsidence,and this method is suitable for three-dimensional surface monitoring in areas with similar geological conditions.The monitoring results have certain reference value.展开更多
Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning...Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.展开更多
文摘AIM: To investigate feasibility and accuracy of near-infrared fluorescence imaging using indocyanine green: nanocolloid for sentinel lymph node (SLN) detection in gastric cancer.METHODS: A prospective, single-institution, phase I feasibility trial was conducted. Patients suffering from gastric cancer and planned for gastrectomy were included. During surgery, a subserosal injection of 1.6 mL ICG:Nanocoll was administered around the tumor. NIR fluorescence imaging of the abdominal cavity was performed using the Mini-FLARE™ NIR fluorescence imaging system. Lymphatic pathways and SLNs were visualized. Of every detected SLN, the corresponding lymph node station, signal-to-background ratio and histopathological diagnosis was determined. Patients underwent standard-of-care gastrectomy. Detected SLNs outside the standard dissection planes were also resected and evaluated.RESULTS: Twenty-six patients were enrolled. Four patients were excluded because distant metastases were found during surgery or due to technical failure of the injection. In 21 of the remaining 22 patients, at least 1 SLN was detected by NIR Fluorescence imaging (mean 3.1 SLNs; range 1-6). In 8 of the 21 patients, tumor-positive LNs were found. Overall accuracy of the technique was 90% (70%-99%; 95%CI), which decreased by higher pT-stage (100%, 100%, 100%, 90%, 0% for respectively Tx, T1, T2, T3, T4 tumors). All NIR-negative SLNs were completely effaced by tumor. Mean fluorescence signal-to-background ratio of SLNs was 4.4 (range 1.4-19.8). In 8 of the 21 patients, SLNs outside the standard resection plane were identified, that contained malignant cells in 2 patients.CONCLUSION: This study shows successful use of ICG:Nanocoll as lymphatic tracer for SLN detection in gastric cancer. Moreover, tumor-containing LNs outside the standard dissection planes were identified.
基金supported by Science and Technology Facilities Council (STFC) under Newton fund (No. ST/N006852/1)Chinese Scholarship Council (CSC) for supporting his study in the UK
文摘Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.
基金the Talent Introduction Project of Anhui University of Science and Technology(ZHYJ202104)Horizontal Cooperation Project(881079,880554,880982)Innovation and Entrepreneurship Project of National College Students(S202310879289,S202310879296,X202310879098,X20231087-9097).
文摘In order to solve the problems of small monitoring range,long time and high cost of existing sedimentation observation methods,based on two-view sentinel No.1 radar images of Guqiao mining area in Huainan City from November 4,2017 to November 28,2017,surface change information was obtained in combination with D-InSAR,and the three-dimensional surface deformation was monitored by two-pass method and single line of sight D-InSAR method.The results show that during the research period of 24 d,the maximum deformation of the mining area reached 71 mm,and the southern subsidence was the most obvious,which was in line with the mining subsidence law.The maximum displacement from the north to the south was about 250 mm,while the maximum displacement from the east to the west was about 80 mm,and the maximum subsidence in the center was 110 mm.It is concluded that D-InSAR technique has a good effect on the inversion of the mining subsidence,and this method is suitable for three-dimensional surface monitoring in areas with similar geological conditions.The monitoring results have certain reference value.
基金supported by the Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources[grant number KF-2021-06-014]the National Natural Scientific Foundation of China[grant number 42201459]+2 种基金the Central Government to Guide Local Scientific and Technological Development[grant number 22ZY1QA005]Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University,Young Doctoral Fund Project of Higher Education Institutions in Gansu Province[grant number 2022QB-058]State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM(2022-03-03).
文摘Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.
