An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI
Michael Ebner,
Guotai Wang,
Wenqi Li,
Michael Aertsen,
Premal A. Patel,
Rosalind Aughwane,
Andrew Melbourne,
Tom Doel,
Steven Dymarkowski,
Paolo De Coppi,
Anna L. David,
Jan Deprest,
Sébastien Ourselin,
Tom Vercauteren
Affiliations
Michael Ebner
Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Corresponding author. Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
Guotai Wang
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Corresponding author. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Wenqi Li
Nvidia, Cambridge, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
Michael Aertsen
Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
Premal A. Patel
Department of Radiology, Great Ormond Street Hospital for Children, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
Rosalind Aughwane
Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
Andrew Melbourne
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Medical Physics and Biomedical Engineering, University College London, London, UK
Tom Doel
Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
Steven Dymarkowski
Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
Paolo De Coppi
Institute of Child Health, University College London, London, UK
Anna L. David
Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium
Jan Deprest
Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium; Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
Sébastien Ourselin
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
Tom Vercauteren
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice.
