Work

Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

Year: 2023

Type: conference-paper

Source: Lecture notes in computer science

DOI: 10.1007/978-3-031-45544-5_7

Authors: Yingcheng Liu, Neerav Karani, S. Mazdak Abulnaga, Junshen Xu, P. Ellen Grant, Esra Abaci Turk, Polina Golland

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Harvard University, Athinoula A. Martinos Center for Biomedical Imaging, Boston Children's Hospital

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: G149491316, Neuroimaging Analysis Center (NAC), Novel MRI Assessment of Placental Structure and Function Throughout Pregnancy, G5238647092, G5940414319, Tackling distribution shifts in machine learning methods for medical image analysis

OpenAlex ID: W4387545353

FWCI: 0.108

Cites: 18

Cited by: 1

Related to: 10