Multi-Task Learning Approach for Unified Biometric Estimation from Fetal
  Ultrasound Anomaly Scans
Mohammad Areeb Qazi Mohammed Talha Alam Ibrahim Almakky Werner Gerhard Diehl Leanne Bricker Mohammad Yaqub
Precise estimation of fetal biometry parameters from ultrasound images is
vital for evaluating fetal growth, monitoring health, and identifying potential
complications reliably. However, the automated computerized segmentation of the
fetal head, abdomen, and femur from ultrasound images, along with the
subsequent measurement of fetal biometrics, remains challenging. In this work,
we propose a multi-task learning approach to classify the region into head,
abdomen and femur as well as estimate the associated parameters. We were able
to achieve a mean absolute error (MAE) of 1.08 mm on head circumference, 1.44
mm on abdomen circumference and 1.10 mm on femur length with a classification
accuracy of 99.91\% on a dataset of fetal Ultrasound images. To achieve this,
we leverage a weighted joint classification and segmentation loss function to
train a U-Net architecture with an added classification head. The code can be
accessed through
\href{https://github.com/BioMedIA-MBZUAI/Multi-Task-Learning-Approach-for-Unified-Biometric-Estimation-from-Fetal-Ultrasound-Anomaly-Scans.git}{\texttt{Github}