Estimation of preterm birth markers with U-Net segmentation network

08/24/2019
by   Tomasz Włodarczyk, et al.
5

Preterm birth is the most common cause of neonatal death. Current diagnostic methods that assess the risk of preterm birth involve the collection of maternal characteristics and transvaginal ultrasound imaging conducted in the first and second trimester of pregnancy. Analysis of the ultrasound data is based on visual inspection of images by gynaecologist, sometimes supported by hand-designed image features such as cervical length. Due to the complexity of this process and its subjective component, approximately 30 preterm deliveries are not correctly predicted. Moreover, 10 preterm deliveries are false-positives. In this paper, we address the problem of predicting spontaneous preterm delivery using machine learning. To achieve this goal, we propose to first use a deep neural network architecture for segmenting prenatal ultrasound images and then automatically extract two biophysical ultrasound markers, cervical length (CL) and anterior cervical angle (ACA), from the resulting images. Our method allows to estimate ultrasound markers without human oversight. Furthermore, we show that CL and ACA markers, when combined, allow us to decrease false-negative ratio from 30 to 18 rely only on gynaecologist's expertise, our method introduce objectively obtained results.

READ FULL TEXT

page 4

page 6

page 7

research
08/16/2020

Spontaneous preterm birth prediction using convolutional neural networks

An estimated 15 million babies are born too early every year. Approximat...
research
11/16/2019

Lightweight Residual Network for The Classification of Thyroid Nodules

Ultrasound is a useful technique for diagnosing thyroid nodules. Benign ...
research
09/06/2020

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

Sickle Cell Disease (SCD) is one of the most common genetic diseases in ...
research
05/11/2022

Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net

Color images are easy to understand visually and can acquire a great dea...
research
09/10/2020

Fully automated analysis of muscle architecture from B-mode ultrasound images with deep learning

B-mode ultrasound is commonly used to image musculoskeletal tissues, but...
research
07/02/2019

Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks

Objective: To test automated in vivo estimation of active and passive sk...
research
09/29/2009

Information tracking approach to segmentation of ultrasound imagery of prostate

The size and geometry of the prostate are known to be pivotal quantities...

Please sign up or login with your details

Forgot password? Click here to reset