ISACHI: Integrated Segmentation and Alignment Correction for Heart Images

We address the problem of cardiovascular shape representation from misaligned Cardiovascular Magnetic Resonance (CMR) images. An accurate 3D representation of the heart geometry allows for robust metrics to be calculated for multiple applications, from shape analysis in populations to precise description and quantification of individual anatomies including pathology. Clinical CMR relies on the acquisition of heart images at different breath holds potentially resulting in a misaligned stack of slices. Traditional methods for 3D reconstruction of the heart geometry typically rely on alignment, segmentation and reconstruction independently. We propose a novel method that integrates simultaneous alignment and segmentation refinements to realign slices producing a spatially consistent arrangement of the slices together with their segmentations fitted to the image data.

[1]  J. Alison Noble,et al.  Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization , 2017, FIMH.

[2]  Rafael Sebastian,et al.  Three-dimensional cardiac computational modelling: methods, features and applications , 2015, Biomedical engineering online.

[3]  Xianghua Xie,et al.  Integrated Segmentation and Interpolation of Sparse Data , 2014, IEEE Transactions on Image Processing.

[4]  Ling Shao,et al.  A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Zhuowen Tu,et al.  Holistically-Nested Edge Detection , 2015, ICCV.

[6]  Enrico G. Caiani,et al.  Nearly automated motion artifacts correction between multi breath-hold short-axis and long-axis cine CMR images , 2014, Comput. Biol. Medicine.

[7]  Jürgen Weese,et al.  Landmark-based elastic registration using approximating thin-plate splines , 2001, IEEE Transactions on Medical Imaging.

[8]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Xianghua Xie,et al.  Registration and Modeling From Spaced and Misaligned Image Volumes , 2016, IEEE Transactions on Image Processing.

[10]  H. Hricak,et al.  Magnetic resonance imaging with respiratory gating: techniques and advantages. , 1984, AJR. American journal of roentgenology.

[11]  B. Mosadegh,et al.  Cardiac 3D Printing and its Future Directions. , 2017, JACC. Cardiovascular imaging.

[12]  Jose Dolz,et al.  3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study , 2016, NeuroImage.

[13]  Vicente Grau,et al.  Cardiac Mesh Reconstruction from Sparse, Heterogeneous Contours , 2017, MIUA.

[14]  Hervé Delingette,et al.  Segmentation and Registration Coupling from Short-Axis Cine MRI: Application to Infarct Diagnosis , 2016, STACOM@MICCAI.

[15]  Daniel Rueckert,et al.  A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion , 2015, Medical Image Anal..

[16]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[17]  Vicente Grau,et al.  Correction of Slice Misalignment in Multi-breath-hold Cardiac MRI Scans , 2016, STACOM@MICCAI.

[18]  Zhiming Luo,et al.  Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation , 2017, ArXiv.

[19]  Shubham Jain,et al.  2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation , 2017, STACOM@MICCAI.

[20]  Vicente Grau,et al.  Surface Mesh Reconstruction from Cardiac MRI Contours , 2018, J. Imaging.

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).