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Tipex - Deformable Lung Registration for Pulmonary Image Analysis of MRI and CT scans flac-Album

Tipex - Deformable Lung Registration for Pulmonary Image Analysis of MRI and CT scans flac-Album

Deformable Lung Registration for Pulmonary Image Analysis of MRI and CT scans
12 Oct 2016
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1Validation of deformable image registration3:13
2fp = up = {up, vp, zp}5:13
3Respiratory motion estimation6:34
4 Deformable multi-modal registration for fusion2:25
5Spatial context for statistical similarity metrics17:20


Recorded October 2014 at IRCAM


This thesis deals with the problem of deformable image registration in the context of lung imaging, and addresses three of the major challenges involved in this challenging application, namely: designing an image similarity for multi-modal scans or scans of locally changing contrast, modelling of complex lung motion, which includes sliding motion, and approximately globally optimal mathematical optimisation to deal with large motion of small anatomical features. The derived methods are applied to two different clinical applications in pulmonary image analysis: motion. Thesis mainly evaluates deformable lung. registration for pulmonary image analysis in MRI and CT scans. A comprehensive. framework combining some novel mathematical formulations and computational. Medical Image Analysis 1998 21: 1-36. Du K. Regional pulmonary function analysis using image registration and 4D CT. thesis, University of Iowa 2013. Gorbunova V. Image registration of lung CT scans for monitoring disease progression. PhD thesis, Copenhagen University 2010. Staring M, Klein S, Reiber JHC, et al. Deformable image registration is an important step in medical image analysis. It enables an automatic labelling of anatomical structures using atlas-based segmentation, motion compensation and. Heinrich, M. Deformable lung registration for pulmonary image analysis of MRI and CT scans. University of Oxford 2013Google Scholar. Hermann, S. Werner, R. High Accuracy Optical Flow for 3D Medical Image Registration Using the Census Cost Function. Deformable image registration DIR is a keystone of the dose accumulation process Figure 1. Particularly, the two most popular DIR methods, demons and the free form deformation FFD methods have been considered. PET-CT and MRI coregistration was used for tumor delineation. Three target volumes were generated. An analysis of variance ANOVA was carried out to assess the impact of each of the DIR methods Table 1 on the DSC and LM error. Using ANOVA contrasts, all methods were tested against all methods. Eppenhof and J. Pluim, Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks, J. Imaging, vol. 5, no. Simonovsky, B. Gutiérrez-Becker, D. Mateus, N. Navab, and N. Komodakis, A deep metric for multimodal registration, Lect. Ferrante, O. Oktay, B. Glocker, and D. Milone, On the adaptability of unsupervised CNN-based deformable image registration to unseen image domains, Lect. Notes Comput. including Subser. MIA: Medical Image Analysis. TIP: IEEE Transactions on Image Processing. TBME: IEEE Transactions on Biomedical Engineering. lung disease, acute pulmonary embolism. Detection rates for pulmonary infiltrates with the basic protocol match CT and make MRI a valuable alternative in par-ticular for children, young patients and pregnant women. The sensitivity for lung nodules reaches 8090 for lesions 4 mm 100 for 8 mm. Both capaci-ties are appreciated in follow-up studies of cystic fibrosis patients using dedicated scores for the extent of disease. Chronic obstructive pulmonary disease COPD is a type of obstructive lung disease characterized by long-term breathing problems and poor airflow. The main symptoms include shortness of breath and cough with sputum production. COPD is a progressive disease, meaning it typically worsens over time. Eventually, everyday activities such as walking or getting dressed become difficult. Chronic bronchitis and emphysema are older terms used for different types of COPD. The term chronic bronchitis is still. Index Terms- GANs, deformable registration, displace-ment eld. Image registration is a fundamental step in most medical im-age analysis problems, and a comprehensive review of algo-rithms can be found in. Conventional registra-tion methods use iterative gradient descent based optimiza-tion using cost functions such as mean square error MSE, normalized mutual information, etc. Thw works in use convolutional stacked au-toencoders CAE to extract features from xed and moving images, and use it in a conventional iterative deformable reg-istration framework. The works of 11, 12, 13, 14. Two sets of images were created to evaluate deformable image registration accuracy. The first set contains CT, T1-, and T2-weighted images from a porcine phantom. The phantom was implanted with ten mm gold markers and then immobilized in a plastic container with movable dividers. The porcine phantom was compressed in 4 different ways and images were acquired in each position. The markers were visible on the CT scans but not the MR scans due to the selected voxel size. Therefore, the markers do not interfere with the registration between MR images and the marker locations can be obtained f. Pulmonary angiography. CT image obtained by using lung window settings at a more inferior level same patient as in the previous image shows a moderately sized area of high attenuation at the periphery of the superior segment of the right lower lobe. This is consistent with pulmonary infarction caused by pulmonary embolism