Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep
Learning Model For The Accurate Caliper Placement To Obtain Sonographic
Measurements Of The Fetal Brain
- URL: http://arxiv.org/abs/2203.14482v1
- Date: Mon, 28 Mar 2022 04:00:22 GMT
- Title: Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep
Learning Model For The Accurate Caliper Placement To Obtain Sonographic
Measurements Of The Fetal Brain
- Authors: H Shankar, A Narayan, S Jain, D Singh, P Vyas, N Hegde, P Kar, A Lad,
J Thang, J Atada, D Nguyen, PS Roopa, A Vasudeva, P Radhakrishnan, S Devalla
- Abstract summary: We propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC)
We leveraged clinically relevant biometric constraints (relationship between caliper points) and domain-relevant data augmentation to improve the accuracy of a U-Net DL model.
For all cases, the mean errors in the placement of the individual caliper points and the computed biometry were comparable to error rates among clinicians.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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 (TC) through the accurate
and automated caliper placement (2 per biometry) by modeling it as a landmark
detection problem. We leveraged clinically relevant biometric constraints
(relationship between caliper points) and domain-relevant data augmentation to
improve the accuracy of a U-Net DL model (trained/tested on: 596 images, 473
subjects/143 images, 143 subjects). We performed multiple experiments
demonstrating the effect of the DL backbone, data augmentation,
generalizability and benchmarked against a recent state-of-the-art approach
through extensive clinical validation (DL vs. 7 experienced clinicians). For
all cases, the mean errors in the placement of the individual caliper points
and the computed biometry were comparable to error rates among clinicians. The
clinical translation of the proposed framework can assist novice users from
low-resource settings in the reliable and standardized assessment of fetal
brain sonograms.
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