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  1. 医学部
  1. 医学部
  2. 学術雑誌掲載論文  (医学部)

Automatic segmentation of male pelvic floor soft tissue structures for anatomical simulation and morphological assessment in lower rectal cancer surgery

http://hdl.handle.net/10458/0002001839
http://hdl.handle.net/10458/0002001839
93faa550-6af9-4070-b682-2c582c0f7f87
名前 / ファイル ライセンス アクション
s10151-025-03218-z.pdf fulltext (8.1 MB)
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アイテムタイプ 学術雑誌論文 / Journal Article(1)
公開日 2025-10-17
タイトル
タイトル Automatic segmentation of male pelvic floor soft tissue structures for anatomical simulation and morphological assessment in lower rectal cancer surgery
言語 en
言語
言語 eng
キーワード
言語 en
キーワード Anatomy
キーワード
言語 en
キーワード Artificial intelligence
キーワード
言語 en
キーワード Image segmentation
キーワード
言語 en
キーワード Pelvic floor
資源タイプ
資源タイプ journal article
アクセス権
アクセス権 open access
著者 Aisu, Y

× Aisu, Y

en Aisu, Y(Personal)
Kyoto University

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Okada, T

× Okada, T

en Okada, T(Personal)
Japanese Osaka Red Cross Hospital

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Itatani, Y

× Itatani, Y

en Itatani, Y(Personal)
Kyoto University

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Masuo, A

× Masuo, A

en Masuo, A(Personal)
Kyoto University

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Tani, R

× Tani, R

en Tani, R(Personal)
Kyoto University

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Fujimoto, K

× Fujimoto, K

en Fujimoto, K(Personal)
Kyoto University

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Kido, A

× Kido, A

en Kido, A(Personal)
University of Toyama

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澤田, 篤郎

× 澤田, 篤郎

WEKO 35206
e-Rad_Researcher 10784796

ja 澤田, 篤郎
宮崎大学

ja-Kana サワダ, アツロウ

en Sawada, Atsuro
University of Miyazaki

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Sakai, Y

× Sakai, Y

en Sakai, Y(Personal)
Kyoto University

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Obama, K

× Obama, K

en Obama, K(Personal)
Kyoto University

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抄録
内容記述タイプ Abstract
内容記述 Background
Pelvic anatomy is a complex network of organs that varies between individuals. Understanding the anatomy of individual patients is crucial for precise rectal cancer surgeries. Therefore, developing technology that can allow visualization of anatomy before surgery is necessary. This study aims to develop an auto-segmentation model of pelvic structures using AI technology and to evaluate the accuracy of the model toward preoperative anatomical understanding.

Methods
Data were collected from 63 male patients who underwent 3D MRI during a preoperative examination for colorectal and urogenital diseases between November 2015 and July 2019 and from 11 healthy male volunteers. Eleven organs and tissues were segmented. The model was developed using a threefold cross-validation process with a total of 59 cases as development data. The accuracy was evaluated with the separately prepared test data using dice similarity coefficient (DSC), true positive rate (TPR), and positive predictive value (PPV) by comparing AI-segmented data with manual-segmented data.

Results
The highest value of DSC, TPR, and PPV were 0.927, 0.909, and 0.948 for the internal anal sphincter (including the rectum), respectively. On the other hand, the lowest values were 0.384, 0.772, and 0.263 for the superficial transverse perineal muscle, respectively. While there were differences among organs, the overall quality of automatic segmentation was maintained in our model, suggesting that the morphological characteristics of the organs may influence the accuracy.

Conclusions
We developed an auto-segmentation model that can independently delineate soft-tissue structures in the male pelvis using 3D T2-weighted MRIs, providing valuable assistance to doctors in understanding pelvic anatomy.
言語 en
書誌情報 en : Techniques in coloproctology

巻 29, 号 1, p. 176, 発行日 2025-10-08
出版者
出版者 Springer Science and Business Media LLC
言語 en
ISSN
収録物識別子タイプ EISSN
収録物識別子 1128045X
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1007/s10151-025-03218-z
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出版タイプ VoR
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