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{this https URL}{\texttt{Github}

我们提出了一种多任务学习方法，通过对超声图像进行分类和分割来准确估计胎儿生物度量参数，实验结果在胎儿超声图像数据集上达到了很高的分类准确率和较小的平均绝对误差。

胎儿超声异常扫描的统一生物识别估计的多任务学习方法

Multi-Task Learning Approach for Unified Biometric Estimation from Fetal  Ultrasound Anomaly Scans

Prenatal ultrasound imaging is the first-choice modality to assess fetal
health. Medical image datasets for AI and ML methods must be diverse (i.e.
diagnoses, diseases, pathologies, scanners, demographics, etc), however there
are few public ultrasound fetal imaging datasets due to insufficient amounts of
clinical data, patient privacy, rare occurrence of abnormalities in general
practice, and limited experts for data collection and validation. To address
such data scarcity, we proposed generative adversarial networks (GAN)-based
models, diffusion-super-resolution-GAN and transformer-based-GAN, to synthesise
images of fetal ultrasound brain planes from one public dataset. We reported
that GAN-based methods can generate 256x256 pixel size of fetal ultrasound
trans-cerebellum brain image plane with stable training losses, resulting in
lower FID values for diffusion-super-resolution-GAN (average 7.04 and lower FID
5.09 at epoch 10) than the FID values of transformer-based-GAN (average 36.02
and lower 28.93 at epoch 60). The results of this work illustrate the potential
of GAN-based methods to synthesise realistic high-resolution ultrasound images,
leading to future work with other fetal brain planes, anatomies, devices and
the need of a pool of experts to evaluate synthesised images. Code, data and
other resources to reproduce this work are available at
https://github.com/budai4medtech/midl2023.

本文利用 GAN 技术生成 256x256 胎儿超声横颞脑平面图像，结果表明 GAN 技术可以生成逼真的高分辨率超声图像，为将来合成其他胎儿脑平面、解剖结构和设备的工作奠定了基础。