559,079 research outputs found

    An Unsupervised Learning Model for Deformable Medical Image Registration

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    We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a convolutional neural network (CNN), and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201

    Robust semi-automated path extraction for visualising stenosis of the coronary arteries

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    Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3-D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets

    Artificial neural network-statistical approach for PET volume analysis and classification

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    Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

    PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

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    In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

    Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

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    Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

    Comparison of Image Registration Based Measures of Regional Lung Ventilation from Dynamic Spiral CT with Xe-CT

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    Purpose: Regional lung volume change as a function of lung inflation serves as an index of parenchymal and airway status as well as an index of regional ventilation and can be used to detect pathologic changes over time. In this article, we propose a new regional measure of lung mechanics --- the specific air volume change by corrected Jacobian. Methods: 4DCT and Xe-CT data sets from four adult sheep are used in this study. Nonlinear, 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration. Approximately 200 annotated anatomical points are used as landmarks to evaluate registration accuracy. Three different registration-based measures of regional lung mechanics are derived and compared: the specific air volume change calculated from the Jacobian (SAJ); the specific air volume change calculated by the corrected Jacobian (SACJ); and the specific air volume change by intensity change (SAI). Results: After registration, the mean registration error is on the order of 1 mm. For cubical ROIs in cubes with size 20 mm ×\times 20 mm ×\times 20 mm, the SAJ and SACJ measures show significantly higher correlation (linear regression, average r2=0.75r^2=0.75 and r2=0.82r^2=0.82) with the Xe-CT based measure of specific ventilation (sV) than the SAI measure. For ROIs in slabs along the ventral-dorsal vertical direction with size of 150 mm ×\times 8 mm ×\times 40 mm, the SAJ, SACJ, and SAI all show high correlation (linear regression, average r2=0.88r^2=0.88, r2=0.92r^2=0.92 and r2=0.87r^2=0.87) with the Xe-CT based sV without significant differences when comparing between the three methods. Conclusion: Given a deformation field by an image registration algorithm, significant differences between the SAJ, SACJ, and SAI measures were found at a regional level compared to the Xe-CT sV in four sheep that were studied
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