A VS ultrasound diagnostic system with kidney image evaluation functions
Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Jiayi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer assisted radiology and surgery - Berlin : Springer, 2006, 18(2022), 2 vom: 05. Okt., Seite 227-246 |
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| Übergeordnetes Werk: |
volume:18 ; year:2022 ; number:2 ; day:05 ; month:10 ; pages:227-246 |
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| DOI / URN: |
10.1007/s11548-022-02759-0 |
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| Katalog-ID: |
SPR049201816 |
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| 100 | 1 | |a Zhou, Jiayi |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A VS ultrasound diagnostic system with kidney image evaluation functions |
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| 520 | |a Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. | ||
| 650 | 4 | |a Robotic ultrasound |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Visual servoing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Koizumi, Norihiro |0 (orcid)0000-0002-1111-9942 |4 aut | |
| 700 | 1 | |a Nishiyama, Yu |4 aut | |
| 700 | 1 | |a Kogiso, Kiminao |4 aut | |
| 700 | 1 | |a Ishikawa, Tomohiro |4 aut | |
| 700 | 1 | |a Kobayashi, Kento |4 aut | |
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| 700 | 1 | |a Fujibayashi, Takumi |4 aut | |
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| 700 | 1 | |a Tsumura, Ryosuke |4 aut | |
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| 700 | 1 | |a Miyazaki, Hideyo |4 aut | |
| 700 | 1 | |a Numata, Kazushi |4 aut | |
| 700 | 1 | |a Nagaoka, Hidetoshi |4 aut | |
| 700 | 1 | |a Iwai, Toshiyuki |4 aut | |
| 700 | 1 | |a Iijima, Hideyuki |4 aut | |
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10.1007/s11548-022-02759-0 doi (DE-627)SPR049201816 (SPR)s11548-022-02759-0-e DE-627 ger DE-627 rakwb eng Zhou, Jiayi verfasserin aut A VS ultrasound diagnostic system with kidney image evaluation functions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. Robotic ultrasound (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Koizumi, Norihiro (orcid)0000-0002-1111-9942 aut Nishiyama, Yu aut Kogiso, Kiminao aut Ishikawa, Tomohiro aut Kobayashi, Kento aut Watanabe, Yusuke aut Fujibayashi, Takumi aut Yamada, Miyu aut Matsuyama, Momoko aut Tsukihara, Hiroyuki aut Tsumura, Ryosuke aut Yoshinaka, Kiyoshi aut Matsumoto, Naoki aut Ogawa, Masahiro aut Miyazaki, Hideyo aut Numata, Kazushi aut Nagaoka, Hidetoshi aut Iwai, Toshiyuki aut Iijima, Hideyuki aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 18(2022), 2 vom: 05. 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10.1007/s11548-022-02759-0 doi (DE-627)SPR049201816 (SPR)s11548-022-02759-0-e DE-627 ger DE-627 rakwb eng Zhou, Jiayi verfasserin aut A VS ultrasound diagnostic system with kidney image evaluation functions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. Robotic ultrasound (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Koizumi, Norihiro (orcid)0000-0002-1111-9942 aut Nishiyama, Yu aut Kogiso, Kiminao aut Ishikawa, Tomohiro aut Kobayashi, Kento aut Watanabe, Yusuke aut Fujibayashi, Takumi aut Yamada, Miyu aut Matsuyama, Momoko aut Tsukihara, Hiroyuki aut Tsumura, Ryosuke aut Yoshinaka, Kiyoshi aut Matsumoto, Naoki aut Ogawa, Masahiro aut Miyazaki, Hideyo aut Numata, Kazushi aut Nagaoka, Hidetoshi aut Iwai, Toshiyuki aut Iijima, Hideyuki aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 18(2022), 2 vom: 05. Okt., Seite 227-246 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:18 year:2022 number:2 day:05 month:10 pages:227-246 https://dx.doi.org/10.1007/s11548-022-02759-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 2 05 10 227-246 |
| allfields_unstemmed |
10.1007/s11548-022-02759-0 doi (DE-627)SPR049201816 (SPR)s11548-022-02759-0-e DE-627 ger DE-627 rakwb eng Zhou, Jiayi verfasserin aut A VS ultrasound diagnostic system with kidney image evaluation functions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. Robotic ultrasound (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Koizumi, Norihiro (orcid)0000-0002-1111-9942 aut Nishiyama, Yu aut Kogiso, Kiminao aut Ishikawa, Tomohiro aut Kobayashi, Kento aut Watanabe, Yusuke aut Fujibayashi, Takumi aut Yamada, Miyu aut Matsuyama, Momoko aut Tsukihara, Hiroyuki aut Tsumura, Ryosuke aut Yoshinaka, Kiyoshi aut Matsumoto, Naoki aut Ogawa, Masahiro aut Miyazaki, Hideyo aut Numata, Kazushi aut Nagaoka, Hidetoshi aut Iwai, Toshiyuki aut Iijima, Hideyuki aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 18(2022), 2 vom: 05. Okt., Seite 227-246 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:18 year:2022 number:2 day:05 month:10 pages:227-246 https://dx.doi.org/10.1007/s11548-022-02759-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 2 05 10 227-246 |
| allfieldsGer |
10.1007/s11548-022-02759-0 doi (DE-627)SPR049201816 (SPR)s11548-022-02759-0-e DE-627 ger DE-627 rakwb eng Zhou, Jiayi verfasserin aut A VS ultrasound diagnostic system with kidney image evaluation functions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. Robotic ultrasound (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Koizumi, Norihiro (orcid)0000-0002-1111-9942 aut Nishiyama, Yu aut Kogiso, Kiminao aut Ishikawa, Tomohiro aut Kobayashi, Kento aut Watanabe, Yusuke aut Fujibayashi, Takumi aut Yamada, Miyu aut Matsuyama, Momoko aut Tsukihara, Hiroyuki aut Tsumura, Ryosuke aut Yoshinaka, Kiyoshi aut Matsumoto, Naoki aut Ogawa, Masahiro aut Miyazaki, Hideyo aut Numata, Kazushi aut Nagaoka, Hidetoshi aut Iwai, Toshiyuki aut Iijima, Hideyuki aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 18(2022), 2 vom: 05. Okt., Seite 227-246 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:18 year:2022 number:2 day:05 month:10 pages:227-246 https://dx.doi.org/10.1007/s11548-022-02759-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 2 05 10 227-246 |
| allfieldsSound |
10.1007/s11548-022-02759-0 doi (DE-627)SPR049201816 (SPR)s11548-022-02759-0-e DE-627 ger DE-627 rakwb eng Zhou, Jiayi verfasserin aut A VS ultrasound diagnostic system with kidney image evaluation functions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. Robotic ultrasound (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Koizumi, Norihiro (orcid)0000-0002-1111-9942 aut Nishiyama, Yu aut Kogiso, Kiminao aut Ishikawa, Tomohiro aut Kobayashi, Kento aut Watanabe, Yusuke aut Fujibayashi, Takumi aut Yamada, Miyu aut Matsuyama, Momoko aut Tsukihara, Hiroyuki aut Tsumura, Ryosuke aut Yoshinaka, Kiyoshi aut Matsumoto, Naoki aut Ogawa, Masahiro aut Miyazaki, Hideyo aut Numata, Kazushi aut Nagaoka, Hidetoshi aut Iwai, Toshiyuki aut Iijima, Hideyuki aut Enthalten in International journal of computer assisted radiology and surgery Berlin : Springer, 2006 18(2022), 2 vom: 05. Okt., Seite 227-246 (DE-627)512299250 (DE-600)2235881-X 1861-6429 nnns volume:18 year:2022 number:2 day:05 month:10 pages:227-246 https://dx.doi.org/10.1007/s11548-022-02759-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 2 05 10 227-246 |
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Zhou, Jiayi @@aut@@ Koizumi, Norihiro @@aut@@ Nishiyama, Yu @@aut@@ Kogiso, Kiminao @@aut@@ Ishikawa, Tomohiro @@aut@@ Kobayashi, Kento @@aut@@ Watanabe, Yusuke @@aut@@ Fujibayashi, Takumi @@aut@@ Yamada, Miyu @@aut@@ Matsuyama, Momoko @@aut@@ Tsukihara, Hiroyuki @@aut@@ Tsumura, Ryosuke @@aut@@ Yoshinaka, Kiyoshi @@aut@@ Matsumoto, Naoki @@aut@@ Ogawa, Masahiro @@aut@@ Miyazaki, Hideyo @@aut@@ Numata, Kazushi @@aut@@ Nagaoka, Hidetoshi @@aut@@ Iwai, Toshiyuki @@aut@@ Iijima, Hideyuki @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049201816</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510061810.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230201s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11548-022-02759-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049201816</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11548-022-02759-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Jiayi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A VS ultrasound diagnostic system with kidney image evaluation functions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robotic ultrasound</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visual servoing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Koizumi, Norihiro</subfield><subfield code="0">(orcid)0000-0002-1111-9942</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nishiyama, Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kogiso, Kiminao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ishikawa, Tomohiro</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kobayashi, Kento</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Watanabe, Yusuke</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fujibayashi, Takumi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yamada, Miyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Matsuyama, Momoko</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsukihara, Hiroyuki</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsumura, Ryosuke</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yoshinaka, Kiyoshi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Matsumoto, Naoki</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ogawa, Masahiro</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Miyazaki, Hideyo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Numata, Kazushi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nagaoka, Hidetoshi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Iwai, Toshiyuki</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Iijima, Hideyuki</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of computer assisted radiology and surgery</subfield><subfield code="d">Berlin : Springer, 2006</subfield><subfield code="g">18(2022), 2 vom: 05. 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Zhou, Jiayi Koizumi, Norihiro Nishiyama, Yu Kogiso, Kiminao Ishikawa, Tomohiro Kobayashi, Kento Watanabe, Yusuke Fujibayashi, Takumi Yamada, Miyu Matsuyama, Momoko Tsukihara, Hiroyuki Tsumura, Ryosuke Yoshinaka, Kiyoshi Matsumoto, Naoki Ogawa, Masahiro Miyazaki, Hideyo Numata, Kazushi Nagaoka, Hidetoshi Iwai, Toshiyuki Iijima, Hideyuki |
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Elektronische Aufsätze |
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vs ultrasound diagnostic system with kidney image evaluation functions |
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A VS ultrasound diagnostic system with kidney image evaluation functions |
| abstract |
Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Purpose An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Methods Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. Results In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. Conclusion While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney’s actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses. © CARS 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Koizumi, Norihiro Nishiyama, Yu Kogiso, Kiminao Ishikawa, Tomohiro Kobayashi, Kento Watanabe, Yusuke Fujibayashi, Takumi Yamada, Miyu Matsuyama, Momoko Tsukihara, Hiroyuki Tsumura, Ryosuke Yoshinaka, Kiyoshi Matsumoto, Naoki Ogawa, Masahiro Miyazaki, Hideyo Numata, Kazushi Nagaoka, Hidetoshi Iwai, Toshiyuki Iijima, Hideyuki |
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| score |
7.399147 |