medical-10

Placenta Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in placenta-segmentation-20

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Use these libraries to find placenta-segmentation-20 models and implementations

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Most implemented papers

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Wenqi Li, S. Ourselin, Tom Kamiel Magda Vercauteren, A. David, J. Deprest, Maria A. Zuluaga, Guotai Wang, M. Aertsen, T. Doel, R. Pratt, P. Patel•Sun Jul 02 2017

A deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

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Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view

D. Rueckert, A. Gómez, Bernhard Kainz, V. Zimmer, E. Skelton, R. Wright, G. Wheeler, S. Deng, N. Ghavami, K. Lloyd, J. Matthew, J. Hajnal, J. Schnabel•Tue Jun 28 2022

The approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation with reduced image artifacts which are beyond the field-of-view of single probes.

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Automatic Segmentation of the Placenta in BOLD MRI Time Series

Eileen Pan, P. Golland, S. M. Abulnaga, Sean I. Young, Katherine I. Hobgood, Clinton Jia Wang, P. Grant, E. Turk•Wed Aug 03 2022

This work proposes 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 and uses a boundary-weighted loss function to accurately capture the placental shape.

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Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

P. Golland, Neel Dey, Neerav Karani, S. M. Abulnaga, E. Turk, P. Grant, Yingcheng Liu, Junshen Xu•Wed Oct 04 2023

The experimental results show that the method improves the overall segmentation accuracy and provides better performance for outliers and hard samples, and improves the temporal coherency of the prediction, which could lead to more accurate computation of temporal placental biomarkers.

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

Eileen Pan, Neel Dey, S. M. Abulnaga, Katherine I. Hobgood, Clinton Jia Wang, Sean I. Young, P. Grant, Esra Abaci Turk, Polina Golland•Thu Nov 30 2023

This work proposes a machine learning segmentation framework for placental BOLD MRI and applies it to segmenting each volume in a time series using a placental-boundary weighted loss formulation and performs a comprehensive evaluation across several popular segmentation objectives.

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