Feasibility and Reproducibility of a Structure‐Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First‐Trimester Nuchal Translucency Assessment Using 3D Ultrasound
Hayan Kwon, Hyewon Hur, Hyun Cheol Cho, Yun ji Jung, Suhra Kim, Ja‐Young Kwon · 2026
Read Feasibility and Reproducibility of a Structure‐Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First‐Trimester Nuchal Translucency Assessment Using 3D Ultrasound by Hayan Kwon, Hyewon Hur, Hyun Cheol Cho, Yun ji Jung, Suhra Kim, Ja‐Young Kwon (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13578527.txt.
Description
Hayan Kwon and co-authors automate the hunt for the exact ultrasound plane that first trimester screening depends on. Published in 2026, the study targets nuchal translucency measurement, a marker for fetal genetic risk, which demands a true mid-sagittal view. In two dimensional scanning that plane is operator luck as much as skill.
Their network, 3D MSP-net, reads the whole three dimensional volume and segments inner skull landmarks to anchor the plane, instead of leaning on facial cues. Specialists graded its planes against careful manual reads and a commercial rule based system. Reproducibility is the prize: the same volume should yield the same plane whoever runs the software. The authors present feasibility evidence for the structure guided route.
Catalog record
| CREATOR | Hayan Kwon, Hyewon Hur, Hyun Cheol Cho, Yun ji Jung, Suhra Kim, Ja‐Young Kwon |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 28 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |