Abstract
Fetal brain segmentation from Magnetic Resonance (MR) images is a fundamental step in brain development study and early diagnosis. Although progress has been made, performance still needs to be improved especially for the images with motion artifacts (due to unpredictable fetal movement) and/or changes of magnetic field. In this paper, we propose a novel confidence-aware cascaded framework to accurately extract fetal brain from MR image. Different from the existing coarse-to-fine techniques, our two-stage strategy aims to segment brain region and simultaneously produce segmentation confidence for each slice in 3D MR image. Then, the image slices with high-confidence scores are leveraged to guide brain segmentation of low-confidence image slices, especially on the brain regions with blurred boundaries. Furthermore, a slice consistency loss is also proposed to enhance the relationship among the segmentations of adjacent slices. Experimental results on fetal brain MRI dataset show that our proposed model achieves superior performance, and outperforms several state-of-the-art methods.
X. Zhang, Z. Cui and C. Chen—Contributed equally.
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Zhang, X. et al. (2021). Confidence-Aware Cascaded Network for Fetal Brain Segmentation on MR Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12903. Springer, Cham. https://doi.org/10.1007/978-3-030-87199-4_55
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