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

1 code implementation • 8 Dec 2023 • S. Mazdak Abulnaga, Neel Dey, Sean I. Young, Eileen Pan, Katherine I. Hobgood, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland

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.

Placenta Segmentation Segmentation +1

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

1 code implementation • 6 Nov 2023 • Zeen Chi, Zhongxiao Cong, Clinton J. Wang, Yingcheng Liu, Esra Abaci Turk, P. Ellen Grant, S. Mazdak Abulnaga, Polina Golland, Neel Dey

We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero.

Time Series

Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

1 code implementation • 5 Oct 2023 • Yingcheng Liu, Neerav Karani, Neel Dey, S. Mazdak Abulnaga, Junshen Xu, P. Ellen Grant, Esra Abaci Turk, Polina Golland

The placenta plays a crucial role in fetal development.

Decision Making Placenta Segmentation +2

AnyStar: Domain randomized universal star-convex 3D instance segmentation

1 code implementation • 13 Jul 2023 • Neel Dey, S. Mazdak Abulnaga, Benjamin Billot, Esra Abaci Turk, P. Ellen Grant, Adrian V. Dalca, Polina Golland

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.

3D Instance Segmentation Domain Adaptation +2

Automatic Segmentation of the Placenta in BOLD MRI Time Series

1 code implementation • 4 Aug 2022 • S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood, Eileen Pan, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland

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.

Placenta Segmentation Time Series +1

Symmetric Volume Maps: Order-Invariant Volumetric Mesh Correspondence with Free Boundary

1 code implementation • 5 Feb 2022 • S. Mazdak Abulnaga, Oded Stein, Polina Golland, Justin Solomon

Although shape correspondence is a central problem in geometry processing, most methods for this task apply only to two-dimensional surfaces.

Volumetric Parameterization of the Placenta to a Flattened Template

1 code implementation • 15 Nov 2021 • S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland

However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult.

Anatomy Local Distortion

CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation

no code implementations • 30 Apr 2019 • Jonathan Rubin, S. Mazdak Abulnaga

We evaluate the results both qualitatively by visual comparison of generated MR to ground truth, as well as quantitatively by training fully convolutional neural networks that make use of generated MR data inputs to perform ischemic stroke lesion segmentation.

Generative Adversarial Network Image-to-Image Translation +3

Placental Flattening via Volumetric Parameterization

1 code implementation • 12 Mar 2019 • S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland

We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume.

Anatomy

Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss

no code implementations • 2 Nov 2018 • S. Mazdak Abulnaga, Jonathan Rubin

We present a fully convolutional neural network for segmenting ischemic stroke lesions in CT perfusion images for the ISLES 2018 challenge.

Ischemic Stroke Lesion Segmentation Lesion Segmentation +1