高詰 佳史 (タカツメ ヨシフミ)

Takatsume, Yoshifumi

写真a

所属(所属キャンパス)

医学部 解剖学教室 (信濃町)

職名

特任助教(有期)

学歴 【 表示 / 非表示

  • 2003年04月
    -
    2006年03月

    京都大学, 農学研究科, 応用生命科学専攻

    大学院, 単位取得退学, 博士後期

学位 【 表示 / 非表示

  • 農学博士, 京都大学, 課程, 2007年03月

 

論文 【 表示 / 非表示

  • Hand motion-aware surgical tool localization and classification from an egocentric camera

    Shimizu T., Hachiuma R., Kajita H., Takatsume Y., Saito H.

    Journal of Imaging (Journal of Imaging)  7 ( 2 )  2021年02月

     概要を見る

    Detecting surgical tools is an essential task for the analysis and evaluation of surgical videos. However, in open surgery such as plastic surgery, it is difficult to detect them because there are surgical tools with similar shapes, such as scissors and needle holders. Unlike endoscopic surgery, the tips of the tools are often hidden in the operating field and are not captured clearly due to low camera resolution, whereas the movements of the tools and hands can be captured. As a result that the different uses of each tool require different hand movements, it is possible to use hand movement data to classify the two types of tools. We combined three modules for localization, selection, and classification, for the detection of the two tools. In the localization module, we employed the Faster R-CNN to detect surgical tools and target hands, and in the classification module, we extracted hand movement information by combining ResNet-18 and LSTM to classify two tools. We created a dataset in which seven different types of open surgery were recorded, and we provided the annotation of surgical tool detection. Our experiments show that our approach successfully detected the two different tools and outperformed the two baseline methods.

  • Photoacoustic lymphangiography before and after lymphaticovenular anastomosis

    Oh A., Kajita H., Matoba E., Okabe K., Sakuma H., Imanishi N., Takatsume Y., Kono H., Asao Y., Yagi T., Aiso S., Kishi K.

    Archives of Plastic Surgery (Archives of Plastic Surgery)  48 ( 3 ) 323 - 328 2021年

    ISSN  22346163

     概要を見る

    Background Lymphaticovenular anastomosis (LVA) is a minimally invasive surgical procedure used to treat lymphedema. Volumetric measurements and quality-of-life assessments are of-ten performed to assess the effectiveness of LVA, but there is no method that provides information regarding postoperative morphological changes in lymphatic vessels and veins after LVA. Photoacoustic lymphangiography (PAL) is an optical imaging technique that visualizes the distribution of light-absorbing molecules, such as hemoglobin or indocyanine green (ICG), and provides three-dimensional images of superficial lymphatic vessels and the venous system simultaneously. In this study, we performed PAL in lymphedema patients before and after LVA and compared the images to evaluate the effect of LVA. Methods PAL was performed using the PAI-05 system in three patients (one man, two women) with lymphedema, including one primary case and two secondary cases, before LVA. ICG fluorescence lymphography was performed in all cases before PAL. Follow-up PAL was performed between 5 days and 5 months after LVA. Results PAL enabled the simultaneous visualization of clear lymphatic vessels that could not be accurately seen with ICG fluorescence lymphography and veins. We were also able to observe and analyze morphological changes such as the width and the number of lymphatic vessels and veins during the follow-up PAL after LVA. Conclusions By comparing preoperative and postoperative PAL images, it was possible to analyze the morphological changes in lymphatic vessels and veins that occurred after LVA. Our study suggests that PAL would be useful when assessing the effect of LVA surgery.

  • Camera Selection for Occlusion-Less Surgery Recording via Training with an Egocentric Camera

    Saito Y., Hachiuma R., Saito H., Kajita H., Takatsume Y., Hayashida T.

    IEEE Access (IEEE Access)  9   138307 - 138322 2021年

     概要を見る

    Recording surgery is an important technique for education and the evaluation of medical treatments. However, capturing targets such as the surgical field, surgical tools, and the surgeon's hands, is almost impossible since these targets are heavily occluded by the surgeon's head and body during a surgery. We used a recording system in which multiple cameras are installed in the surgical lump, supposing at least one camera would capture the target without occlusion. As this system records multiple video sequences, we address the task to select a best view camera automatically. Recently, learning-based approaches in a fully supervised manner have been proposed for this task, but these previous approaches completely rely on manual annotation of the training data. In this paper, we focus on the eye tracker mounted on the surgeon's head, which can capture the recording targets without occlusion. Employing this first-person-view video synchronized with multiple videos of the surgical lump, we propose a novel camera selection approach using a self-supervised learning framework. In experiments, we created a dataset composed of four different breast surgery. Our extended experiments showed that our approach successfully switched to the best camera view without manual annotation and achieved competitive accuracy compared with conventional supervised methods. Also, our approach yielded effective visual representations comparable to state-of-the-art self-supervised learning frameworks.

  • Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings

    Hachiuma R., Shimizu T., Saito H., Kajita H., Takatsume Y.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))  12263 LNCS   419 - 428 2020年

    ISSN  9783030597153

     概要を見る

    © 2020, Springer Nature Switzerland AG. Recording surgery in operating rooms is an essential task for education and evaluation of medical treatment. However, recording the desired targets, such as the surgery field, surgical tools, or doctor’s hands, is difficult because the targets are heavily occluded during surgery. We use a recording system in which multiple cameras are embedded in the surgical lamp, and we assume that at least one camera is recording the target without occlusion at any given time. As the embedded cameras obtain multiple video sequences, we address the task of selecting the camera with the best view of the surgery. Unlike the conventional method, which selects the camera based on the area size of the surgery field, we propose a deep neural network that predicts the camera selection probability from multiple video sequences by learning the supervision of the expert annotation. We created a dataset in which six different types of plastic surgery are recorded, and we provided the annotation of camera switching. Our experiments show that our approach successfully switched between cameras and outperformed three baseline methods.

  • Surgery recording without occlusions by multi-view surgical videos

    Shimizu T., Oishi K., Hachiuma R., Kajita H., Takatsume Y., Saito H.

    VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications)  5   837 - 844 2020年

    ISSN  9789897584022

     概要を見る

    Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Recording surgery is important for sharing various operating techniques. In most surgical rooms, fixed surgical cameras are already installed, but it is almost impossible to capture the surgical field because of occlusion by the surgeon’s head and body. In order to capture the surgical field, we propose the installation of multiple cameras in a surgical lamp system, so that at least one camera can capture the surgical field even when the surgeon’s head and body occlude other cameras. In this paper, we present a method for automatic viewpoint switching from multi-view surgical videos, so that the surgical field can always be recorded. We employ a method for learning-based object detection from videos for automatic evaluation of the surgical field from multi-view surgical videos. In general, frequent camera switching degrades the video quality of view (QoV). Therefore, we apply Dijkstra’s algorithm, widely used in the shortest path problem, as an optimization method for this problem. Our camera scheduling method works so that camera switching is not performed for the minimum frame we specified, and therefore the surgical field observed in the entire video is maximized.

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総説・解説等 【 表示 / 非表示

  • リンパ浮腫のいろは 光超音波イメージングによるリンパ管へのアプローチ

    鈴木 悠史, 梶田 大樹, 渡部 紫秀, 岡部 圭介, 貴志 和生, 佐久間 恒, 高詰 佳史, 今西 宣晶, 相磯 貞和

    日本形成外科学会会誌 ((一社)日本形成外科学会)  41 ( 7 ) 399 - 400 2021年07月

    ISSN  0389-4703

  • リンパ浮腫に対する最新研究と治療戦略 光超音波イメージングによるリンパ管同定

    鈴木 悠史, 梶田 大樹, 渡部 紫秀, 岡部 圭介, 佐久間 恒, 今西 宣晶, 高詰 佳史, 関口 博之, 浅尾 恭史, 八木 隆行, 相磯 貞和, 貴志 和生

    リンパ学 (日本リンパ学会)  44 ( 1 ) 28 - 31 2021年07月

    ISSN  0910-4186

  • web会議ツールを利用した救命のための侵襲的手技に関するオンライン講義の試み

    佐藤 幸男, 葉 季久雄, 金子 靖, 高詰 佳史, 山元 良, 佐々木 淳一

    日本外傷学会雑誌 ((一社)日本外傷学会)  35 ( 2 ) 152 - 152 2021年05月

    ISSN  1340-6264

  • 光超音波でみるリンパ管の微細構造とその臨床応用

    鈴木 悠史, 梶田 大樹, 渡部 紫秀, 岡部 圭介, 佐久間 恒, 高詰 佳史, 今西 宣晶, 貴志 和生

    日本手外科学会雑誌 ((一社)日本手外科学会)  38 ( 1 ) LS9 - 2 2021年04月

    ISSN  2185-4092

  • 光超音波イメージングと超高周波超音波で描出されるリンパ管・静脈像の比較検討

    渡部 紫秀, 鈴木 悠史, 梶田 大樹, 今西 宣晶, 高詰 佳史, 中島 由佳理, 浅尾 恭史, 八木 隆行, 相磯 貞和, 貴志 和生

    日本マイクロサージャリー学会学術集会プログラム・抄録集 ((一社)日本マイクロサージャリー学会)  47回   153 - 153 2020年11月

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競争的研究費の研究課題 【 表示 / 非表示

  • 低侵襲化手術に必要な解剖学的知識の伝承をより効率化するための学習ツールの開発

    2021年04月
    -
    2024年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 高詰 佳史, 基盤研究(C), 補助金,  研究代表者