The Role of Fetal Brain Magnetic Resonance Imaging in Current Fetal Medicine

In open spina bifida we studied the use of MRI for the assessment of the posterior fossa and prevalence of supratentorial anomalies before and after in utero repair. New postprocessing techniques were applied to evaluate fetal brain development in this population compared to controls. In fetuses with congenital diaphragmatic hernia, we evaluated the brain development in comparison to controls. Diffusion weighted imaging was applied to study difference between fetuses with proven first trimester cytomegalovirus infection and controls. Finally, we investigated the value of third trimester fetal brain MRI after treatment for complicated monochorionic diamniotic pregnancies

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previously used to improve segmentation accuracy, yet not been formulated in a consistent mathematical framework. Hence, we also propose a theoretical formulation of test-time augmentation, where a distribution of the prediction is estimated by Monte Carlo simulation with prior distributions of parameters in an image acquisition model that involves image transformations and noise. We compare and combine our proposed aleatoric uncertainty with model uncertainty. Experiments with segmentation of fetal brains and brain tumors from 2D and 3D Magnetic Resonance Images (MRI) showed that 1) the test-time augmentation-based aleatoric uncertainty provides a better uncertainty estimation than calculating the test-time dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions, and 2) our test-time augmentation outperforms a single-prediction baseline and dropout-based multiple predictions.Comment: 13 pages, 8 figures, accepted by NeuroComputin

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only tumor cores in one MR sequence were annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.Comment: 11 pages, 11 figure

Edematous Ileocecal Valve Mimicking Incomplete Reduction after Intussusception

Teaching Point: An edematous ileocecal valve may mimic a residual intussusception after reduction. Differential diagnosis is important for therapeutic implications

Cortical Surface Matching of the Fetal Brain Pre and Post Fetal Surgery for Open Spina Bifida

Introduction: Fetal surgery has become a clinical reality, even for non-lethal conditions such as open spina bifidayelomeningocele, the spinal cord extrudes into a cereberospinal fluid (CSF) filled sac 1, 2. It is associated with brain anomalies such as hindbrain herniation and variable degrees of ventriculomegaly. Prenatalrepair yields better outcomes compared to postnatal surgery3. Nonetheless, mechanical tissue damage of brain parenchyma due to ventriculomegaly and damage to the neural tracts lead to abnormal white matter development, as demonstrated by diffusion weighted imaging studies4-9. This may lead to altered gyrification patterns in MMC patients. Gyrification, measured by magnetic resonance imaging (MRI), correlates with motor and cognitive function in infants, children and adolescents with who have undergone postnatal closure 6. Evaluation of cognitiveand motor function in fetuses who have had prenatal surgery, is performed only after birth, with deficits becoming more evident with increasing age. Clinicians therefore urgently need early fetal brain imaging methods that can predict the cognitive and motor challenges that fetuses may encounter after birth. We aim to demonstrate that longitudinal quantitative MRI measurement of cortical gyrification is possible, before and after repair. We will also demonstrate the curvature (curvedness and shape index) of cerebellum and ventricles before and after surgery. Methods: T2-weighted single-shot fast spin-echo (SSFSE) was performed of the fetal brain in multiple containing an axial, coronal and sagittal planewith 3mm slice thicknesson women with , both before surgery (n=12, 23+6 1+7 weeks, (22+1–25+6)) and after surgery (n=12, 26+1 1+3 weeks, (24+1– 29+4))acquisition time thirty-forty minutes. Initial diagnosis of open spina bifida was made on mid-trimester ultrasound. Fetuses affected by aneuploidy or with structural anomalies outside the CNS were excluded. A novel automated super resolution reconstruction (SRR) algorithm10, 11 was used to build 3D volumes of the fetal brain based on the 2D stacks that were acquired in different directions. Rigid slice-to-volume registration correcting for fetal motion was used to generate an SRR image in standard anatomical orientation, from which we automatically segmented white matter, ventricles, and cerebellum using template brain segmentations12, 13. 14make us of 12Brain masks for pre-operative SRR volumes were resampled from their corresponding post-operative MMC masks after affine and non-rigid alignment. All masks where manually corrected and meshes were generated using ITK-Snap14. A rigid coherent point drift algorithm was applied to find an initial correspondence for the intrasubject cortical, cerebellar and ventricle regions before and after surgery. Joint spectral matching (JSM) was then used to find the correspondence for the intrasubject at those two different time points. In JSM a dual layered graph was produced whereby layers correspond to the surface of the white matter, cerebellum or ventricles of each subject. The correspondence links from the initial intrasubject matching, connecting both layers to produce a set of shared eigenmodes of the surfaces. After mapping the post-operative surface to the pre-operative surface using JSM, we computed the change in parameters at the vertex of each mesh to explore longitudinal cortical gyrification, and curvature (curvedness and shape index) of the 12. Results: Figure 1 illustrates five spectral modes for the white matter, ventricles and cerebellum of a fetus before surgery (24 weeks), and therafter (26 weeks). Although the meshes are quite different in the three-dimensional space, with respect to different levels of folding, variation in shape, surface area and volume, they have similar representations in the spectral domain. Figure 2 shows the curvedness and shape index in the white matter, cerebellum and ventricles before and after fetal surgery. JSM allows us to map the mean curvatures of each mesh to compute changes in the mean and to generate the shape index.Figure 3 shows maps of mean curvature of a fetus pre and post-surgery. Positive values are depicted in red/yellow and represent gyri (convex structures), and negative values in blue represent sulci (concave) structures. JSM allows mapping of mean curvatures from the post-op to the pre-op space, computing the changes in mean curvature between these two time points in the pre-op space.Figure 4 illustrates the shape index histogram for white matter, showing the differences in gyri and sulci between the pre and post-operative MMC brain.Discussion: Surface-based matching provides additional information about changes in growth and gyrification of the fetal brain compared to measurement of total volume and shape change. This may be useful in evaluating changes in cerebral growth of MMC fetuses before and after fetal surgery. Spectral graph matching is a promising tool for matching shapes with significant differences in cortical folding, surface area, and volume, but with similar representations in the spectral domain such as depicted with fetuses before and after surgery12, 15. Future work may be able to better explore the physiological and mechanical properties contributing to the differences observed in brain growth and development in the context of fetal surgery. Conclusion: Novel analysis of fetal longitudinal correspondence of white matter, and development of specific regions of the brain as secondary gyri emerges, in the context of fetal surgery is demonstrated. This tool allows the measurement of the shape and growth of the white matter surface may help establish longitudinal growth trajectories. References: 1. Rethmann, C., et al., Evolution of posterior fossa and brain morphology after in utero repair of open neural tube defects assessed by MRI. Eur Radiol, 2017. 27(11): p. 4571-4580. 2. Zarutskie, A., et al., Prenatal brain imaging for predicting need for postnatal hydrocephalus treatment in fetuses that had neural tube defect repair in utero. Ultrasound Obstet Gynecol, 2019. 53(3): p. 324-334. 3. Adzick, N.S.T., E.A.; Spong, C. Y.; Brock III, J. W.; Burrows, P. K.; Johnson, M. P.; Howell, R. N.; Farrell, J. N.; Dabrowiak, M.E.; Sutton, L.N.; Gupta, N.; Tulipan, N.B.; D’Alton, M.E.; Farmer, D.L., A Randomised Trial of Prenatal versus Postnatal Repair of Myelomeningocele. N Engl J Med, 2011. 364: p. 993-1004. 4. Juranek, J., et al., Neocortical reorganization in spina bifida. Neuroimage, 2008. 40(4): p. 1516-22. 5. Juranek, J. and M.S. Salman, Anomalous development of brain structure and function in spina bifida myelomeningocele. Developmental Disabilities Research Reviews, 2010. 16(1): p. 23-30. 6. Treble, A., et al., Functional significance of atypical cortical organization in spina bifida myelomeningocele: relations of cortical thickness and gyrification with IQ and fine motor dexterity. Cereb Cortex, 2013. 23(10): p. 2357-69. 7. Hasan, K.M., et al., White matter microstructural abnormalities in children with spina bifida myelomeningocele and hydrocephalus: a diffusion tensor tractography study of the association pathways. J Magn Reson Imaging, 2008. 27(4): p. 700-9. 8. Mignone Philpott, C., et al., Diffusion-weighted imaging of the cerebellum in the fetus with Chiari II malformation. AJNR Am J Neuroradiol, 2013. 34(8): p. 1656-60. 9. Woitek, R., et al., Fetal diffusion tensor quantification of brainstem pathology in Chiari II malformation. Eur Radiol, 2016. 26(5): p. 1274-83. 10. Ebner, M., et al., An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. 2018. p. 313-320. 11. Ebner, M., et al., An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage, 2019. 12. Orasanu, E., et al., Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching. Brain Behav, 2016. 6(8): p. e00488. 13. Kuklisova-Murgasova, M., et al., A dynamic 4D probabilistic atlas of the developing brain. Neuroimage, 2011. 54(4): p. 2750-63. 14. Yushkevich, P.A., et al., User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage, 2006. 31(3): p. 1116-28. 15. Lomabert, H., Spopring J., and Siddiqi K., Diffeomorphic spectral matching of cortical surafaces. Inf Process Med Imaging, 2013. 7917: p. 376-289

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta [version 2; peer review: 2 approved]

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA