Automatic Segmentation of Twin Regions in Mo-Di Placentae Based on Geometric Analysis of Spatiotemporal BOLD MRI Signals

Shnitzer, Tal; Mazdak, Abulnaga, S.; Bibbo, Carolina; Grant, P. Ellen; Golland, Polina; Solomon, Justin; Türk, Esra Abacı

doi:10.58530/2022/0728

Cited by 2 publications

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“…A limitation of relying only on segmentations is that they can only be used to quantify whole-organ signal changes, such as mean T2⋆ or mean BOLD increase. One is often interested in assessing functional differences within subregions of the placenta, for example across twins in Mo-Di pregnancies Shnitzer et al, 2022), within vessels (Torrents-Barrena et al, 2019a), or across cotyledons . Localized analysis requires deformable registration to track changes within these regions.…”

Section: Discussionmentioning

confidence: 99%

Untitled

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%

Untitled

2023

Melba

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Section: Discussionmentioning

confidence: 99%

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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Automatic Segmentation of Twin Regions in Mo-Di Placentae Based on Geometric Analysis of Spatiotemporal BOLD MRI Signals

Cited by 2 publications

References 0 publications

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

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