成果報告書詳細
管理番号20110000001122
タイトル*平成22年度中間年報 がん超早期診断・治療機器の総合研究開発/超早期高精度診断システムの研究開発:病理画像等認識技術の研究開発 病理画像等認識基礎技術の研究開発(定量的病理診断を可能とする病理画像認識技術)(日本電気株式会社)
公開日2011/8/30
報告書年度2010 - 2010
委託先名日本電気株式会社
プロジェクト番号P10003
部署名バイオテクノロジー・医療技術部
和文要約和文要約等以下本編抜粋:1. 研究開発の内容及び成果等 (1)-2 データベースツールの制作とデータベースの構築 肝細胞がんについて、超早期の定義を含めた概念を共通化するとともに、これらの情報を格納できるデータベースの構造を模索した。 まず超早期について、現在の診断(判定)基準などを調査、検討を行い、次の3つを定義し分類することが有効であると判断した。 臨床的超早期肝細胞がん:腫瘍の大きさ、浸潤状態などから臨床的な早期と定義されるもの。病理形態学的超早期がん:組織の形態において、良性病変・感染症あるいは正常と類似した境界領域の肝細胞がん。
英文要約Title: Research and Development Project for pathological image recognition technology, Research and Development Project to develop basic technologies for recognizing pathology images, Pathological image analysis technology to enable a quantitative pathological diagnosis, (FY2010-FY2012) FY2010 Annual Report
(1) Research and Development of Image Recognition and Quantification Technology for Pathological Images: The development of the computer-aided system for a pathological image-reading is needed in order to support pathologist. The goal of our project is to establish the quantification method for liver cancer cells. Liver cancer cells have many features in multiple image resolution levels. Thus the area segmentation preprocessing is effective to analyze images in an appropriate image resolution for each part. In this study, segmentation method of cancer area by using difference of stainability due to cancer is proposed and tested with one case. The results show that rough segmentation of H&E stained pathological image is possible with further investigations by considering different staining conditions. Also we developed a clustering program based on a self-organizing map to extract the features from multi-dimensional information such as stainability, ballooning and fibrosis etc. (2) Development of High Quality Image Compression Technology for Pathological Images: Image compression is necessary for pathological images because the raw images of 1G bytes are too large to storage and to analyze. As pathological image compression needs to satisfy the requirements for handling multiple resolution images and multi-spectrum images, we propose to develop modified JPEG2000. In this study, we clarify the new requirements of the image compression for analysis of pathological image of liver cancer. Scalable image compression technology should be introduces to satisfy to analyze multi features of images that depend on the objects such as nuclei, cytoplasm and cell construction. To evaluate proposed image compression method, we develop the modified JPEG2000 software that is able to handle multi-spectrum images and high quantized images.
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