scholarly journals Does intra-articular injection of adipose-derived stem cells improve cartilage mass? A case report on a 3D MRI quantitative evaluation of cartilage in knee osteoarthritis using SYNAPSE VINCENT: a case report

2020 ◽  
Author(s):  
Ayano Kuwasawa ◽  
Kotaro Nihei
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Image Analysis ◽  
Case Report ◽  
The State ◽  
3D Image ◽  
Analysis Software ◽  
3D Image Analysis ◽  
Image Analysis Software ◽  
Before And After

Abstract Background: Mesenchymal stem cells (MSC) are currently in focus because of the possibility of cartilage regeneration through several ways, including MSC sheets. However, there is no published report that visualizes cartilage in three dimensions. Here, we report a case of improved cartilage volume. We purified and cultured adipose-derived mesenchymal stem cells (ASC) and then performed ASC therapy by directly injecting these cells into the articular cartilage. Cartilage was quantitatively evaluated before and after injection using a three-dimensional (3D) image analysis software based on the MRI imagery.Case presentation: The patient, a 55-year-old woman, experienced pain in both knees and was diagnosed with osteoarthritis of the knee. We performed ASC therapy in both knees at our hospital and quantitatively evaluated cartilage before and after the treatment using the 3D image analysis software “SYNAPSE VINCENT”.Conclusions: For the quantitative analysis of cartilage, SYNAPSE VINCENT visualizes the state of cartilage in a high-definition 3D image, which is excellent for understanding the state of the disease and explaining it to the patient. Though there is room for debate about the reproducibility of errors, etc., SYNAPSE VINCENT would be useful as a clinical tool for regenerative medicine.

scholarly journals Does intra-articular injection of adipose-derived stem cells improve cartilage mass? A case report on a 3D MRI quantitative evaluation of cartilage in knee osteoarthritis using SYNAPSE VINCENT: a case report

2020 ◽  
Author(s):  
Ayano Kuwasawa ◽  
Kotaro Nihei
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Image Analysis ◽  
Case Report ◽  
The State ◽  
3D Image ◽  
Analysis Software ◽  
3D Image Analysis ◽  
Image Analysis Software ◽  
Before And After

Abstract Background: Mesenchymal stem cells (MSC) are currently in focus because of the possibility of cartilage regeneration through several ways, including MSC sheets. However, there is no published report that visualizes cartilage in three dimensions. Here, we report a case of improved cartilage volume. We purified and cultured adipose-derived mesenchymal stem cells (ASC) and then performed ASC therapy by directly injecting these cells into the articular cartilage. Cartilage was quantitatively evaluated before and after injection using a three-dimensional (3D) image analysis software based on the MRI imagery.Case presentation: The patient, a 55-year-old woman, experienced pain in both knees and was diagnosed with osteoarthritis of the knee. We performed ASC therapy in both knees at our hospital and quantitatively evaluated cartilage before and after the treatment using the 3D image analysis software “SYNAPSE VINCENT”.Conclusions: For the quantitative analysis of cartilage, SYNAPSE VINCENT visualizes the state of cartilage in a high-definition 3D image, which is excellent for understanding the state of the disease and explaining it to the patient. Though there is room for debate about the reproducibility of errors, etc., SYNAPSE VINCENT would be useful as a clinical tool for regenerative medicine.

scholarly journals Does intra-articular injection of adipose-derived stem cells improve cartilage mass? A case report using three-dimensional image analysis software in knee osteoarthritis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Ayano Kuwasawa ◽  
Kotaro Nihei
Keyword(s):  
Stem Cells ◽  
Image Analysis ◽  
Stem Cell ◽  
Mesenchymal Stem Cell ◽  
Three Dimensional ◽  
Analysis Software ◽  
Mesenchymal Stem Cell Therapy ◽  
Dimensional Image ◽  
Image Analysis Software ◽  
Before And After

Abstract Background Mesenchymal stem cells are currently a research focus because of the possibility of cartilage regeneration through several mechanisms, including mesenchymal stem cell sheets. However, there are no published reports visualizing cartilage in three dimensions. Here, we report a case of improved cartilage volume. We purified and cultured adipose-derived mesenchymal stem cells and then performed adipose-derived mesenchymal stem cell therapy by directly injecting these cells into the articular cartilage. Cartilage was quantitatively evaluated before and after injection using three-dimensional image analysis software based on the magnetic resonance imaging. Case presentation The patient, a 55-year-old Japanese woman, experienced pain in both knees and was diagnosed with osteoarthritis of the knee. We performed adipose-derived mesenchymal stem cell therapy in both knees at our hospital and quantitatively evaluated cartilage before and after the treatment using the three-dimensional image analysis software “SYNAPSE VINCENT”. Conclusions Preoperatively, the cartilage defect area was 33.59 mm2 in the femur and 122.31 mm2 in the tibia; however, 12 months postoperatively, it improved to 13.59 mm2 and 51.43 mm2, respectively. Furthermore, the preoperative femur and tibia volumes were 9.58 mL and 3.82 mL, respectively; however, 12 months postoperatively, these values improved to 10.00 mL and 4.17 mL, respectively. For the quantitative analysis of cartilage, SYNAPSE VINCENT visualizes the state of cartilage in a high-definition three-dimensional image, which is excellent for understanding the state of the disease and explaining it to the patient. Although SYNAPSE VINCENT can only analyze the thickness of cartilage, and the reproducibility of the error is debatable, SYNAPSE VINCENT would be useful as a clinical tool for regenerative medicine. We have shown in this case report the promising effects of adipose-derived stem cell intraarticular injections in treating osteoarthritis and the use of new diagnostic instruments.

scholarly journals Anatomical Characteristics of Garcinia lucida (Vesque) and Scorodophloeus zenkeri (Harms) Wood and Debarking Response in the South Region Cameroon

2017 ◽  
Vol 6 (4) ◽  
pp. 132
Author(s):  
Marie Caroline Momo Solefack ◽  
Hans Beeckman ◽  
Lucie Felicite Temgoua ◽  
Ghislain Kenguem Kinjouo
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Software ◽  
Anatomical Changes ◽  
Image Analysis Software ◽  
Conductive Zone ◽  
Before And After

The aim of this work was to investigate the possible anatomical changes of Garcinia lucida and Scorodophloeus zenkeri after the removal of their bark. Debarking was done on individuals of each species at 1.30 m from the soil. The wound was rectangular in shape with 30 cm side. There was a follow-up every three months for nine months during which the survival and rate of regeneration of the bark were recorded. A block of cube was cut from the regenerated and intact wood of species for microtomy and microscopy activities. On the cross-section of each wood, vessel features like density and diameter were measured before and after wounding. Semi-automatic measurements were made using the SpectrumSee digital image analysis software. In the wood of the two species, it appeared that the density of the vessels before debarking was significantly comparable to the density after debarking, while the diameter of vessels in the regenerated wood was smaller. The cambial area increased slightly in the rainy season for all species. After nine months all the species started the restoration of their conductive zone. G. lucida heals its wound more rapidly than S. zenkeri.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software
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