Using 9200 laser particle size analyzer and KYKY-2800 scanning electron microscope, particle sizes and cellular morphology of Radix Astragalus and Radix Isatidis ultrafine powders were observed. According to the resu...Using 9200 laser particle size analyzer and KYKY-2800 scanning electron microscope, particle sizes and cellular morphology of Radix Astragalus and Radix Isatidis ultrafine powders were observed. According to the results, the particle size of 89. 1 % of Radix Astragalus ultrafine powders ranged from 1.729 [xm to 44.938 ｜xm, Z ）50 =4.368 ｜xm; the particle size of 93.411% of Radix Isatidis ultrafine powders ranged from 1.510 [xm to 44.938 ｜xm, Z ）50 = 8 .7 2 6 [xm. Radix As-tragalus and Radix Isatidis ultrafine powders were pulverized completely without intact cellular morphology. The antibacterial activity of Radix Astragalus and Radix Isatidis ultrafine powders against chicken-derived E. coli （078） was investigated. The results indicated that Radix Astragalus and Radix Isatidis ultrafine powders exhibited higher antibacterial activity against chicken-derived E. coli （078 ） compared with the corresponding coarse powders. This study laid a solid foundation for the development and application of Chinese medicine ultrafine powder preparations.展开更多
Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research,teaching,and clinical practice.Medical image segmentation requires sophisticated computerize...Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research,teaching,and clinical practice.Medical image segmentation requires sophisticated computerized quantifications and visualization tools.Recently,with the development of artificial intelligence(AI)technology,tumors or organs can be quickly and accurately detected and automatically contoured from medical images.This paper introduces a platform-independent,multi-modality image registration,segmentation,and 3D visualization program,named artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization(AIMIS3D).YOLOV3 algorithm was used to recognize prostate organ from T2-weighted MRI images with proper training.Prostate cancer and bladder cancer were segmented based on U-net from MRI images.CT images of osteosarcoma were loaded into the platform for the segmentation of lumbar spine,osteosarcoma,vessels,and local nerves for 3D printing.Breast displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast bra.Brain vessel from multimodality MRI images was segmented by using model-based transfer learning for 3D printing and naked eye 3D visualization in AIMIS3D platform.展开更多
基金Oupported by Spark Program of the Ministry of Science and Technology of China(2015GA620002)Science and Technology Support Program of Science and Technology Department of Hebei Province(12220408D,14966610D)+2 种基金Post-award Grant Program from the Department of Science and Technology of Hebei Province(15926620H)Project of Shijiazhuang Municipal Science and Technology Bureau(141200603A)Project of Chengde Municipal Science and Technology Bureau(2015N0001)
文摘Using 9200 laser particle size analyzer and KYKY-2800 scanning electron microscope, particle sizes and cellular morphology of Radix Astragalus and Radix Isatidis ultrafine powders were observed. According to the results, the particle size of 89. 1 % of Radix Astragalus ultrafine powders ranged from 1.729 [xm to 44.938 ｜xm, Z ）50 =4.368 ｜xm; the particle size of 93.411% of Radix Isatidis ultrafine powders ranged from 1.510 [xm to 44.938 ｜xm, Z ）50 = 8 .7 2 6 [xm. Radix As-tragalus and Radix Isatidis ultrafine powders were pulverized completely without intact cellular morphology. The antibacterial activity of Radix Astragalus and Radix Isatidis ultrafine powders against chicken-derived E. coli （078） was investigated. The results indicated that Radix Astragalus and Radix Isatidis ultrafine powders exhibited higher antibacterial activity against chicken-derived E. coli （078 ） compared with the corresponding coarse powders. This study laid a solid foundation for the development and application of Chinese medicine ultrafine powder preparations.
文摘Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research,teaching,and clinical practice.Medical image segmentation requires sophisticated computerized quantifications and visualization tools.Recently,with the development of artificial intelligence(AI)technology,tumors or organs can be quickly and accurately detected and automatically contoured from medical images.This paper introduces a platform-independent,multi-modality image registration,segmentation,and 3D visualization program,named artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization(AIMIS3D).YOLOV3 algorithm was used to recognize prostate organ from T2-weighted MRI images with proper training.Prostate cancer and bladder cancer were segmented based on U-net from MRI images.CT images of osteosarcoma were loaded into the platform for the segmentation of lumbar spine,osteosarcoma,vessels,and local nerves for 3D printing.Breast displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast bra.Brain vessel from multimodality MRI images was segmented by using model-based transfer learning for 3D printing and naked eye 3D visualization in AIMIS3D platform.
