| Project/Area Number |
15K21336
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Medical systems
Biomedical engineering/Biomaterial science and engineering
|
|---|
| Research Institution | Saitama Medical University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Renkei-kenkyūsha) |
Tokiya Abe 慶應義塾大学, 医学部病理学教室 (10422552)
|
|---|
| Project Period (FY) |
2015-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 病理画像処理 / 定量化 / whole slide imaging / ハイパースペクトル画像 / マルチスペクトル / 色補正 / 構造認識 / Computer Aided Diagnosis / 病理画像 / 医用画像処理 / 移植腎 / EVG染色 / 糸球体 / 色素量推定 |
|---|
| Outline of Final Research Achievements |
This study aimed to develop a function estimation method using structure recognition in transplanted renal pathology tissue images and machine learning. By targeting whole slide images, we determined the cortex and medullary regions in a transplanted renal pathology specimen, extracted the glomerulus, and developed a quantification method for the number of peripheral fibers. We also examined a structure recognition method using spectral images, which proved that it could recognize the organization structure more accurately than conventional methods using the RGB value. Finally, we estimated the transplant renal functions with clinical data , which showed the possibility that this method can estimate the functions at a maximum accuracy of 85%.
|
