SHIMOMURA Mei

写真a

Affiliation

Faculty of Medicine College Hospital Department of radiology

Title

Assistant Professor

External Link

 

Papers 【 display / non-display

  • Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader’s experience Reviewed International journal

    Azuma M., Nakada H., Takei M., Nakamura K., Katsuragawa S., Shinkawa N., Terada T., Masuda R., Hattori Y., Ide T., Kimura A., Shimomura M., Kawano M., Matsumura K., Meiri T., Ochiai H., Hirai T.

    Emergency Radiology   29 ( 2 )   317 - 328   2022.4

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Emergency Radiology  

    Purpose: The evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists’ performance. Methods: The dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures. A three-dimensional faster region-based CNN was trained and evaluated by using tenfold cross-validation. For an observer performance study to investigate the effect of CNN outputs on radiologists’ performance, 30 thoracic CT scans (28 scans with 90 acute rib fractures and 2 without rib fractures) which were not included in the development dataset were used. Observer performance study involved eight radiologists who evaluated CT images first without and second with CNN outputs. The diagnostic performance was assessed by using figure of merit (FOM) values obtained from the jackknife free-response receiver operating characteristic (JAFROC) analysis. Results: When radiologists used the CNN output for detection of rib fractures, the mean FOM value significantly increased for all readers (0.759 to 0.819, P = 0.0004) and for displaced (0.925 to 0.995, P = 0.0028) and non-displaced fractures (0.678 to 0.732, P = 0.0116). At all rib levels except for the 1st and 12th ribs, the radiologists’ true-positive fraction of the detection became significantly increased by using the CNN outputs. Conclusion: The CNN specialized for the detection of acute rib fractures on CT images can improve the radiologists’ diagnostic performance regardless of the type of fractures and reader’s experience. Further studies are needed to clarify the usefulness of the CNN for the detection of acute rib fractures on CT images in actual clinical practice.

    DOI: 10.1007/s10140-021-02000-6

    Scopus

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Presentations 【 display / non-display

  • 肺乳頭腺腫の1切除例

    中村 咲和、川野 真嗣、下村 明、中田 博、東 美菜子、前田 亮、綾部 貴典、魏 峻洸

    第195回日本医学放射線学会九州地方会 

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    Event date: 2022.6.18 - 2022.6.19

    Language:Japanese   Presentation type:Oral presentation (general)  

  • 右肺静脈瘤の診断にFour-Dimensional CTAが有用であった1例

    明利陸征、増田梨絵、古小路英二、榮 建文

    第192回日本医学放射線学会九州地方会  2021.2.7 

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    Event date: 2021.2.7

    Language:Japanese   Presentation type:Oral presentation (general)  

  • リンパ腫様肉芽腫症の診断に至ったメトトレキサート関連リンパ増殖性疾患の1例

    今田真希、下村 明、中田 博、榮 建文、川口 剛、魏 峻洸

    第192回日本医学放射線学会九州地方会  2021.2.7 

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    Event date: 2021.2.7

    Language:Japanese   Presentation type:Oral presentation (general)