  In this paper, we propose an end-to-end multi-task neural network called
FetalNet with an attention mechanism and stacked module for spatio-temporal
fetal ultrasound scan video analysis. Fetal biometric measurement is a standard
examination during pregnancy used for the fetus growth monitoring and
estimation of gestational age and fetal weight. The main goal in fetal
ultrasound scan video analysis is to find proper standard planes to measure the
fetal head, abdomen and femur. Due to natural high speckle noise and shadows in
ultrasound data, medical expertise and sonographic experience are required to
find the appropriate acquisition plane and perform accurate measurements of the
fetus. In addition, existing computer-aided methods for fetal US biometric
measurement address only one single image frame without considering temporal
features. To address these shortcomings, we propose an end-to-end multi-task
neural network for spatio-temporal ultrasound scan video analysis to
simultaneously localize, classify and measure the fetal body parts. We propose
a new encoder-decoder segmentation architecture that incorporates a
classification branch. Additionally, we employ an attention mechanism with a
stacked module to learn salient maps to suppress irrelevant US regions and
efficient scan plane localization. We trained on the fetal ultrasound video
comes from routine examinations of 700 different patients. Our method called
FetalNet outperforms existing state-of-the-art methods in both classification
and segmentation in fetal ultrasound video recordings.
