Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI
Recently, magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation, which proves to be challenging because of the high variability of its position, orientation, shape and appearance. Moreover, image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus, as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field, so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of \(71.95\pm 19.79\,\%\) for healthy fetuses with a fixed scan sequence and \(66.89\pm 15.35\,\%\) for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.
KeywordsFetal MRI Placenta Segmentation
We thank NVIDIA for the donation of a Tesla K40 GPU. Medical Interaction Toolkit (http://mitk.org/) was used for some of the figures. This research was supported by the NIHR Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Furthermore, this work was supported by Wellcome Trust and EPSRC IEH award 102431, FP7 ERC 319456, and EPSRC EP/N024494/1. A. Alansary and K. Kamnitsas are supported by the Imperial College PhD Scholarship.
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