# Automatic Segmentation of the Placenta in BOLD MRI Time Series

arXiv: https://arxiv.org/abs/2208.02895 · HTML: https://mlsanity.com/paper/2208.02895/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI](https://mlsanity.com/paper/1906.08827/) (2019)
- [Volumetric Parameterization of the Placenta to a Flattened Template](https://mlsanity.com/paper/2111.07900/) (2021)

## Built on by (most cited first, 5 of 5)

- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Boundary-weighted logit consistency improves calibration of segmentation networks](https://mlsanity.com/paper/2307.08163/) (2023)
- [Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series](https://mlsanity.com/paper/2311.02874/) (2023)
- [Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series](https://mlsanity.com/paper/2310.03870/) (2023)
- [Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series](https://mlsanity.com/paper/2312.05148/) (2023)

## Abstract (arXiv)

Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess oxygen transport within the placenta and has emerged as a promising tool to study placental function. Measuring signal changes over time requires segmenting the placenta in each volume of the time series. Due to the large number of volumes in the BOLD time series, existing studies rely on registration to map all volumes to a manually segmented template. As the placenta can undergo large deformation due to fetal motion, maternal motion, and contractions, this approach often results in a large number of discarded volumes, where the registration approach fails. In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series. We use a boundary-weighted loss function to accurately capture the placental shape. Our model is trained and tested on a cohort of 91 subjects containing healthy fetuses, fetuses with fetal growth restriction, and mothers with high BMI. We achieve a Dice score of 0.83+/-0.04 when matching with ground truth labels and our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series. Our code and trained model are available at https://github.com/mabulnaga/automatic-placenta-segmentation.