2003 Fiscal Year Final Research Report Summary
Research on the analysis of histopathologic images (Development of hisopathologic diagnosis support system for hepatocellular carcinoma)
| Project/Area Number |
14580832
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Shibaura Institute of Technology |
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Principal Investigator |
TAKAHASHI Masanobu Shibaura Institute of Technology, Faculty of Systems Engineering, Associate Professor, システム工学部, 助教授 (20338312)
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| Co-Investigator(Kenkyū-buntansha) |
NAKANO Masayuki Chiba Hospital, Division of Clinical Investigation, Division Chief, 研究検査科, 科長 (00092073)
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| Project Period (FY) |
2002 – 2003
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| Keywords | Tissue / Nucleus / Contour / Extraction / Image processing / Liver / Histopathologic diagnosis / Diagnosis support |
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| Research Abstract |
The purpose of this research was to develop a histopathologic diagnosis support system forhepatocellular carcinoma. We developed a support system especially for differential diagnosis between non-cancer and extremely well-differentiated hepatocellular carcinoma (ewHCC)
First, a following three-step method to extract the positions of hepatocellular nuclei were developed. (i) Candidates extraction step: the candidates of nuclear positions are extracted. (ii)Contours extraction step: a novel contour extraction method, which we call "Radial Snakes" has been developed and used. (iii)Screening step: most of wrongly extracted positions are removed based on the energy values. Using this method, 84.8% of the nuclear positions were correctly extracted with 12.2% of the positions wrongly extracted. As a result, the time required to correct the wrong positions using a graphical user interface (GUI) was drastically reduced.
Nuclear density and ratio of high nuclear density area are proposed as the two features calculated using the extracted positions. The average of the sensitivity and specificity was over 90% for both features, showing the effectiveness of these features for differential diagnosis between normal and ewHCC.
A GUI was developed that enables a user to extract the nuclear positions, to correct the positions, and to calculate the proposed features by simple button operations. The total time for processing one image and extracting the features was about 2 minutes. This system would be helpful in actual diagnosis. We are going to improve the system so that it can be used in more medical organizations.
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