# Estimation of preterm birth markers with U-Net segmentation network

arXiv: https://arxiv.org/abs/1908.09148 · HTML: https://mlsanity.com/paper/1908.09148/

## Key facts

- Cited by 7 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation](https://mlsanity.com/paper/2601.15572/) (2026)
- [Convolutional Neural Networks in Orthodontics: a review](https://mlsanity.com/paper/2104.08886/) (2021)
- [Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans](https://mlsanity.com/paper/2201.06406/) (2022)
- [PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk](https://mlsanity.com/paper/2509.07042/) (2025)
- [Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI](https://mlsanity.com/paper/2606.20172/) (2026)
- [From Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction](https://mlsanity.com/paper/2606.23005/) (2026)

## Abstract (arXiv)

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% of spontaneous preterm deliveries are not correctly predicted. Moreover, 10% of the predicted 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%. Finally, contrary to the current approaches to diagnostics methods that rely only on gynaecologist's expertise, our method introduce objectively obtained results.