Over the past decades, low-energy electron accelerators have been used worldwide for surface curing and sterilization. The beam nonuniformity is an important parameter of the low-energy electron beam with large cross-...Over the past decades, low-energy electron accelerators have been used worldwide for surface curing and sterilization. The beam nonuniformity is an important parameter of the low-energy electron beam with large cross-sections. A simple and accurate measurement system of nonuniformity for the low-energy electron beam with large cross-sections was developed. The main concept consists in the measurement of nonuniformity, which is realized by using a linear actuator to drive two scanning wires through the beam's cross-sections at a fixed speed. The beam distribution can be obtained by sending/collecting the current signals to/from the Data Acquisition (DAQ) software on a laptop by a USB DAQ card. This device is very convenient for the performance testing of a new accelerator at the manufacturer's site. The distribution of the homemade low voltage electron accelerator EBS-300-50 was measured and evaluated.展开更多
Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-s...Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.Large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference展开更多
文摘Over the past decades, low-energy electron accelerators have been used worldwide for surface curing and sterilization. The beam nonuniformity is an important parameter of the low-energy electron beam with large cross-sections. A simple and accurate measurement system of nonuniformity for the low-energy electron beam with large cross-sections was developed. The main concept consists in the measurement of nonuniformity, which is realized by using a linear actuator to drive two scanning wires through the beam's cross-sections at a fixed speed. The beam distribution can be obtained by sending/collecting the current signals to/from the Data Acquisition (DAQ) software on a laptop by a USB DAQ card. This device is very convenient for the performance testing of a new accelerator at the manufacturer's site. The distribution of the homemade low voltage electron accelerator EBS-300-50 was measured and evaluated.
文摘Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.Large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference
