A 3D WUCT system using a single ultrasound transducer is designed and automated. The dataset consist of the WUCT reconstruction results dataset used to train U-Net based semantic segmentation model.  Also, dataset i) to study the effect of increase in the number of virtual transducer on reconstruction quality and, ii) effect of variation in the applied pulse width on the reconstruction are provided. The U-Net based semantic segmentation model is trained and used to evaluate dice coefficient corresponding to the phantom’s actual profile and reconstructed profile.

Dataset Files

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[1] Ankur Kumar, Mayank Goswami, "WUCT dataset", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/ehrj-z518. Accessed: Mar. 22, 2025.
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doi = {10.21227/ehrj-z518},
url = {http://dx.doi.org/10.21227/ehrj-z518},
author = {Ankur Kumar; Mayank Goswami },
publisher = {IEEE Dataport},
title = {WUCT dataset},
year = {2023} }
TY - DATA
T1 - WUCT dataset
AU - Ankur Kumar; Mayank Goswami
PY - 2023
PB - IEEE Dataport
UR - 10.21227/ehrj-z518
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Ankur Kumar, Mayank Goswami. (2023). WUCT dataset. IEEE Dataport. http://dx.doi.org/10.21227/ehrj-z518
Ankur Kumar, Mayank Goswami, 2023. WUCT dataset. Available at: http://dx.doi.org/10.21227/ehrj-z518.
Ankur Kumar, Mayank Goswami. (2023). "WUCT dataset." Web.
1. Ankur Kumar, Mayank Goswami. WUCT dataset [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/ehrj-z518
Ankur Kumar, Mayank Goswami. "WUCT dataset." doi: 10.21227/ehrj-z518