中口 俊哉

The aim of this work is to overcome the narrow surgical field of view problem in minimally invasive surgery. We achieve this by combining multiple views of the camera-retractable trocar which can obtain surgical viewpoints different from laparoscopic view. However, the accuracy and time are essential factors in this process. Therefore, we tend to improve the accuracy of a hybrid tracking-mosaicking approach which can combine several views at high speed. Two improvements are presented and analyzed here. The first improvement utilizes two sharping methodologies to enhance the image quality. This enhancement, in turn, improves the interest point extraction process and increases the number of extracted points. In the second enhancement, the tracking accuracy is improved by applying a filtering methodology to select the set of valid flow vectors only. This process reduces the tracking error which may accumulate during tracking. The experimental evaluation, shows that these improvements enhance the final mosaicking accuracy and allows us to construct a more accurate expanded view.

In order to repair the boundary depressions caused by juxtapleural nodules and improve the lung segmentation accuracy, we propose a new boundary correction method for lung parenchyma. Firstly, the top- hat filter is used to enhance the image contrast; Secondly, we employ the Ostu algorithm for image binarization; Thirdly, the connected component labeling algorithm is utilized to remove the main trachea; Fourthly, the initial mask image is obtained by morphological region filling algorithm; Fifthly, the boundary tracing algorithm is applied to extract the initial lung contour; Afterwards, we design a sudden change degree algorithm to modify the initial lung contour; Finally, the complete lung parenchyma image is obtained. The novelty is that sudden change degree algorithm can detect the inflection points more accurately than other methods, which contributes to repairing lung contour efficiently. The experimental results show that the proposed method can incorporate the juxtapleural nodules into the lung parenchyma effectively, and the precision is increased by 6.46% and 2.72% respectively compared with the other two methods, providing favorable conditions for the accurate detection of pulmonary nodules and having important clinical value.

我々は臨床における実効性の高いコンピュータ支援検出(CAD)ソフトウェアの開発を目的として、各研究グループで開発したCADソフトウェアを東大病院内のCADソフトウェア開発用データベース症例にて機械学習および性能評価を行う共同研究プロジェクト(UTH CAD Challenge)を2014年8月より継続している。2015年7月よりUTH CAD Challenge 2015を実施し、これまでに頭部MRA画像の脳動脈瘤検出ならびに胸部CT画像の肺結節検出の2部門に各3グループよりソフトウェアが提出された。本稿ではUTH CAD Challenge 2015プロジェクトの現況について報告する。(著者抄録)