Motion corrected 3D reconstruction of the fetal thorax from prenatal MRI

In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature

Adaptive scan strategies for fetal MRI imaging using slice to volume techniques

© 2015 IEEE.In this paper several novel methods to account for fetal movements during fetal Magnetic Resonance Imaging (fetal MRI) are explored. We show how slice-to-volume reconstruction methods can be used to account for motion adaptively during the scan. Three candidate methods are tested for their feasibility and integrated into a computer simulation of fetal MRI. The first alters the main orientation of the stacks used for reconstruction, the second stops if too much motion occurs during slice acquisition and the third steers the orientation of each slice individually. Reconstruction informed adaptive scanning can provide a peak signal-to-noise ratio (PSNR) improvement of up to 2 dB after only two stacks of scanned slices and is more efficient with respect to the uncertainty of the final reconstruction

Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels

Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and must be completed by highly specialized indi-viduals. Super-resolution reconstruction of fetal MRI has become standard for processing such data as it improves image quality and resolution. However, dif-ferent pipelines result in slightly different outputs, further complicating the gen-eralization of segmentation methods aiming to segment super-resolution data. Therefore, we propose using transfer learning with noisy multi-class labels to automatically segment high resolution fetal brain MRIs using a single set of seg-mentations created with one reconstruction method and tested for generalizability across other reconstruction methods. Our results show that the network can auto-matically segment fetal brain reconstructions into 7 different tissue types, regard-less of reconstruction method used. Transfer learning offers some advantages when compared to training without pre-initialized weights, but the network trained on clean labels had more accurate segmentations overall. No additional manual segmentations were required. Therefore, the proposed network has the potential to eliminate the need for manual segmentations needed in quantitative analyses of the fetal brain independent of reconstruction method used, offering an unbiased way to quantify normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202

Recherches sur les Cucurbitac\ue9es de Madagascar

Volume: 16Start Page: 127End Page: 33