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Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep Learning Model For The Accurate Caliper Placement To Obtain Sonographic Measurements Of The Fetal Brain. (arXiv:2203.14482v2 [eess.IV] UPDATED)
Aug. 2, 2022, 2:13 a.m. | Hari Shankar, Adithya Narayan, Shefali Jain, Divya Singh, Pooja Vyas, Nivedita Hegde, Purbayan Kar, Abhi Lad, Jens Thang, Jagruthi Atada, Duy Nguyen,
cs.CV updates on arXiv.org arxiv.org
Multiple studies have demonstrated that obtaining standardized fetal brain
biometry from mid-trimester ultrasonography (USG) examination is key for the
reliable assessment of fetal neurodevelopment and the screening of central
nervous system (CNS) anomalies. Obtaining these measurements is highly
subjective, expertise-driven, and requires years of training experience,
limiting quality prenatal care for all pregnant mothers. In this study, we
propose a deep learning (DL) approach to compute 3 key fetal brain biometry
from the 2D USG images of the transcerebellar plane …
More from arxiv.org / cs.CV updates on arXiv.org
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