Automated template-based brain localization and extraction for fetal brain MRI reconstruction

ABSTRACT Most fetal brain MRI reconstruction algorithms rely only on brain tissue‐relevant voxels of low‐resolution (LR) images to enhance the quality of inter‐slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi‐automatic task. We have proposed in this work to use age‐matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template‐to‐slice block matching and deformable slice‐to‐template registration. Our template‐based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter‐slice motion correction, and super‐resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice‐to‐template registration and propagation of the brain mask slice‐by‐slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice‐by‐slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks. HIGHLIGHTSWe offer a template‐based fetal brain localization, extraction and segmentation.We reconstruct fetal brain MRI in a standard common coordinate space.We achieve brain extraction in addition to localization success rate of 93%.Brain segmentation accuracy (Dice overlap) compared to manual delineation is 94.5%.We report fetal brain tissue volume growth maps using atlas‐based segmentation.

[1]  Daniel Rueckert,et al.  Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[2]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[3]  Simon K. Warfield,et al.  A template-to-slice block matching approach for automatic localization of brain in fetal MRI , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[4]  Amir Alansary,et al.  Flexible Reconstruction and Correction of Unpredictable Motion from Stacks of 2D Images , 2015, MICCAI.

[5]  Tricia Walker,et al.  Computer science , 1996, English for academic purposes series.

[6]  Xavier Pennec,et al.  Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration , 2002, ECCV.

[7]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

[8]  Daniel Rueckert,et al.  Automated fetal brain segmentation from 2D MRI slices for motion correction , 2014, NeuroImage.

[9]  Colin Studholme,et al.  Early folding patterns and asymmetries of the normal human brain detected from in utero MRI. , 2012, Cerebral cortex.

[10]  Alan C. Evans,et al.  Delayed cortical development in fetuses with complex congenital heart disease. , 2013, Cerebral cortex.

[11]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[12]  Dinggang Shen,et al.  Learning-Based Meta-Algorithm for MRI Brain Extraction , 2011, MICCAI.

[13]  G. Langs,et al.  Fully Automated Brain Extraction and Orientation in Raw Fetal MRI , 2013 .

[14]  Simon K. Warfield,et al.  Simultaneous Truth and Performance Level Estimation Through Fusion of Probabilistic Segmentations , 2013, IEEE Transactions on Medical Imaging.

[15]  Colin Studholme,et al.  Automatic Template-based Brain Extraction in Fetal MR Images , 2013 .

[16]  Daniel Rueckert,et al.  Localisation of the Brain in Fetal MRI Using Bundled SIFT Features , 2013, MICCAI.

[17]  Colin Studholme,et al.  BTK: An open-source toolkit for fetal brain MR image processing , 2013, Comput. Methods Programs Biomed..

[18]  J. Besag On the Statistical Analysis of Dirty Pictures , 1986 .

[19]  Colin Studholme,et al.  International Journal of Developmental Neuroscience Growth Trajectories of the Human Fetal Brain Tissues Estimated from 3d Reconstructed in Utero Mri , 2022 .

[20]  Simon K. Warfield,et al.  Multi-atlas multi-shape segmentation of fetal brain MRI for volumetric and morphometric analysis of ventriculomegaly , 2012, NeuroImage.

[21]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[22]  Sotirios A. Tsaftaris,et al.  Medical Image Computing and Computer Assisted Intervention , 2017 .

[23]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[24]  Alan C. Evans,et al.  Quantitative in vivo MRI measurement of cortical development in the fetus , 2011, Brain Structure and Function.

[25]  Mary A. Rutherford,et al.  Reconstruction of fetal brain MRI with intensity matching and complete outlier removal , 2012, Medical Image Anal..

[26]  Colin Studholme,et al.  Segmentation of the cortex in fetal MRI using a topological model , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[27]  Onur Afacan,et al.  Fetal MRI: A Technical Update with Educational Aspirations. , 2014, Concepts in magnetic resonance. Part A, Bridging education and research.

[28]  C. Studholme,et al.  3D global and regional patterns of human fetal subplate growth determined in utero , 2010, Brain Structure and Function.

[29]  Simon K. Warfield,et al.  A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth , 2017, Scientific Reports.

[30]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[31]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[32]  Meritxell Bach Cuadra,et al.  Automatic brain extraction in fetal MRI using multi-atlas-based segmentation , 2015, Medical Imaging.

[33]  Colin Studholme,et al.  Intersection Based Motion Correction of Multislice MRI for 3-D in Utero Fetal Brain Image Formation , 2010, IEEE Transactions on Medical Imaging.

[34]  Xavier Bresson,et al.  Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction , 2014, MICCAI.

[35]  Simon K. Warfield,et al.  Construction of a Deformable Spatiotemporal MRI Atlas of the Fetal Brain: Evaluation of Similarity Metrics and Deformation Models , 2014, MICCAI.

[36]  Jayaram K. Udupa,et al.  New Variants of a Method of MRI Scale Normalization , 1999, IPMI.

[37]  Xavier Bresson,et al.  An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization , 2015, NeuroImage.

[38]  Daniel Rueckert,et al.  Fast Volume Reconstruction from Motion Corrupted Stacks of 2D Slices , 2015, IEEE Transactions on Medical Imaging.

[39]  Daniel Rueckert,et al.  Automatic quantification of normal cortical folding patterns from fetal brain MRI , 2014, NeuroImage.

[40]  Isabelle Bloch,et al.  Automatic segmentation of head structures on fetal MRI , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[41]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[42]  W. B. Johnson,et al.  Extensions of Lipschitz mappings into Hilbert space , 1984 .

[43]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[44]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[45]  Daniel Rueckert,et al.  MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies , 2007, IEEE Transactions on Medical Imaging.