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Yuuki Shida

dblp:304/4381 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0003-2698-9577ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 77% Robot navigation and mapping · 23%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › medical robotics
robotic ultrasound
0.512021
Heart Position Estimation based on Bone Distribution toward Autonomous Robotic Fetal Ultrasonography · ICRA 2021
Robotics › Robot navigation and mapping › active perception
autonomous scanning
0.112021
Heart Position Estimation based on Bone Distribution toward Autonomous Robotic Fetal Ultrasonography · ICRA 2021

Methods — techniques the papers use, named apart from their topics

shadow detection · 1.0anatomical structure analysis · 1.03d bone position estimation · 1.0
YearPublicationVenuePosition
2021 Heart Position Estimation based on Bone Distribution toward Autonomous Robotic Fetal Ultrasonography
abstract
Autonomous fetal ultrasonography with a robotic ultrasound (US) can potentially solve the issue of the shortage of ob-gyn physicians in prenatal care. In fetal cardiac diagnosis, acoustic shading derived from the fetal skeleton makes the scanning procedure using a robotic US cumbersome. We hypothesize that fetal bone distribution can be used for determining the fetal position and for subsequently estimating the heart position. This paper serves a method for estimating fetal heart position for autonomous cardiac diagnosis. Our proposed system is comprised of following three processes. First, the fetal bone distribution is generated by following steps: detect shadow areas derived from fetal bones; calculate the three-dimensional (3D) position of the bones; and identify bone types (skull, spine, and others) based on its shadow features. Next, the fetal head position and body axis are estimated based on the generated 3D bone distribution. Finally, the fetal chest position and thoracic sagittal axis of the fetus are estimated based on anatomical structures, and the position of the heart is determined. We validated the proposed method by evaluating the accuracies of bone detection and heart position estimation with three pregnant volunteers. The false positive rate of bone detection is less than 10%, which is sufficient for the proposed method; further, the estimation error for the heart position was within an acceptable error of 15 mm. These results demonstrate the sufficient accuracy of the proposed estimation method and suggest the potential of autonomous fetal cardiac diagnosis using robotic US scanning.
Yuuki Shida, Ryosuke Tsumura, Takabumi Watanabe, Hiroyasu Iwata
ICRA1