| Co-Investigator(Kenkyū-buntansha) |
NAKAMOTO Masahiko 大阪大学, 医学系研究科, 助教 (00380634)
YAMAZAKI Takaharu 大阪大学, 臨床医工学融合研究教育センター, 特任准教授 (40432546)
TADA Yukio 神戸大学, 工学系研究科, 教授 (70135812)
HORI Masatoshi 大阪大学, 医学系研究科, 助教 (00346206)
TOMIYAMA Noriyuki 大阪大学, 医学系研究科, 准教授 (50294070)
SUGANO Nobuhiko 大阪大学, 医学系研究科, 寄附講座教授 (70273620)
SUGAMOTO Kazuomi 大阪大学, 医学系研究科, 寄附講座教授 (40294061)
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| Budget Amount *help |
¥114,790,000 (Direct Cost: ¥88,300,000、Indirect Cost: ¥26,490,000)
Fiscal Year 2013: ¥22,360,000 (Direct Cost: ¥17,200,000、Indirect Cost: ¥5,160,000)
Fiscal Year 2012: ¥22,490,000 (Direct Cost: ¥17,300,000、Indirect Cost: ¥5,190,000)
Fiscal Year 2011: ¥22,100,000 (Direct Cost: ¥17,000,000、Indirect Cost: ¥5,100,000)
Fiscal Year 2010: ¥22,100,000 (Direct Cost: ¥17,000,000、Indirect Cost: ¥5,100,000)
Fiscal Year 2009: ¥25,740,000 (Direct Cost: ¥19,800,000、Indirect Cost: ¥5,940,000)
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| Research Abstract |
Methods for constructing computational anatomy models, which represent inter-patient variability of organ shapes, locations, and their interrelations, were developed based on statistical analysis of organ shape data of a number of patients. These models were combined with Bayesian inference to perform automated segmentation of multiple abdominal organs and musculoskeletal structures of the hip from CT images. Diagnostic and therapeutic patient data were further added to the organ shape data to incorporate diagnostic and therapeutic decision support modeling into the computational anatomy models. By combining Bayesian inference or machine learning technologies with the models, we confirmed that automated diagnosis of the liver fibrosis and surgical planning of the hip implant surgery are possible in an accurate manner.
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