A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI

Specktor-Fadida, Bella; Link-Sourani, Daphna; Ferster-Kveller, Shai; Ben‐Sira, Liat; Miller, Elka; Bashat, Dafna Ben; Joskowicz, Leo

doi:10.1007/978-3-030-87735-4_18

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…L CE , L Dice ), demonstrating the benefit in capturing the placenta boundary accurately. Additional loss functions exist that we did not compare with, such as the distance transform-based boundary loss of Kervadec et al (2021), and the boundary contour-based loss functions of Specktor-Fadida et al (2021) and Jurdi et al (2021). Similar to our baselines, these loss functions are additive and aim to improve boundary capture and reduce the Hausdorff distance.…”

Section: Discussionmentioning

confidence: 99%

“…Placenta segmentation in structural MRI. Machine learning segmentation models for the placenta have been previously proposed and include both semi-automatic (Wang et al, 2015) and automatic (Alansary et al, 2016;Torrents-Barrena et al, 2019a;Specktor-Fadida et al, 2021) approaches. While semi-automatic methods have achieved success in predicting segmentation label maps with high accuracy, these approaches are infeasible for segmenting BOLD MRI time series due to the large number of volumes that would require manual annotation.…”

Section: Related Workmentioning

confidence: 99%

“…Torrents-Barrena et al (2019a) developed a segmentation framework based on super-resolution and a support vector machine and validated it using a singleton and twin cohort of T2w MRI. Specktor-Fadida et al (2021) focused on transferring segmentation networks across MRI sequences using a self-training model yielding successful segmentation of steady-state precession MRI sequences. For a detailed treatment of fetal MRI segmentation, we refer the reader to the survey by Torrents-Barrena et al (2019b).…”

Section: Related Workmentioning

confidence: 99%

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Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series

Abulnaga,

Dey,

Young

et al. 2023

Melba

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Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and maternal motion, contractions, and hyperoxia-induced intensity changes. Current BOLD placenta segmentation methods warp a manually annotated subject-specific template to the entire time series. However, as the placenta is a thin, elongated, and highly non-rigid organ subject to large deformations and obfuscated edges, existing work cannot accurately segment the placental shape, especially near boundaries. In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series. We use a placental-boundary weighted loss formulation and perform a comprehensive evaluation across several popular segmentation objectives. 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. Biomedically, our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series. We further find that boundary-weighting increases placental segmentation performance by 8.3% and 6.0% Dice coefficient for the cross-entropy and Signed Distance Transform objectives, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series

Abulnaga,

Dey,

Young

et al. 2023

Melba

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“…Machine learning segmentation models for the placenta have been previously proposed and include both semi-automatic [23] and automatic [5,19,10,17] approaches. While semi-automatic methods have achieved success in predicting segmentation label maps with high accuracy, these approaches are infeasible for segmenting BOLD MRI time series due to the large number of volumes.…”

Section: Introductionmentioning

confidence: 99%

“…Torrents-Barrena et al [19] proposed a model based on superresolution and an SVM and validated on a singleton and twin cohort of T2w MRI. Spektor-Fadida et al [17] tackled the problem of domain transfer by a self-training model and demonstrated successful segmentation of FIESTA and TRUFI sequences. For a more detailed treatment of segmentation methods in fetal MRI, we refer the reader to the survey by Torrents-Barrena et al [20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of the Placenta in BOLD MRI Time Series

Mazdak¹,

Young²,

Hobgood³

et al. 2022

Preprint

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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.

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