TU‐CD‐BRA‐02: Comparing Mutual Information and Gradient Magnitude Metrics for Deformable Image Registration
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Purpose: Deformable image registration between CT and MRI remains a challenge due to variability in contrast, homogeneity, and field of view. This study aims to determine whether an optimized Mutual Information (MI) or Gradient Magnitude (GM) metric provides favorable results in the head and neck region. Methods: Head and neck CT and MR images were acquired for 14 patients as part of an MRI simulation study with a dual echo mDixon (in phase) T1 fast field echo sequence. Each pair of scans is registered with MRI as the reference image using the GM and MI metrics, where MI was optimized by controlling histogram parameters. Results are observed visually, and quantitatively measured by comparing Dice coefficient and Hausdorff distance of manually contoured bone structures. Results: Using the optimized GM metric, the average Dice coefficient is 0.52 ± 0.16; the average Hausdorff distance is 7.5 ± 1.9 mm. Using the optimized MI metric, the average Dice coefficient is 0.55 ± 0.085; the average Hausdorff distance is 6.6 ± 1.4 mm. Conclusion: While both metrics performed similarly, the MI metric has a higher Dice coefficient than the GM metric, and a lower Hausdorff distance. This suggests image registration with the Mutual Information metric may be preferred.Keywords:
Hausdorff distance
Sørensen–Dice coefficient
Image registration
Dice
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Sørensen–Dice coefficient
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Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter $\kappa$ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMf Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve an adaptive training based on the accuracy of the class. Through experiments conducted on four datasets using a five-fold cross validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.
Dice
Sørensen–Dice coefficient
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Cosine similarity
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Medical image registration has important value in clinical application.This paper investigats the popular registration method based on maxmal mutual information.Further,studies the method of normalized mutual information.By the simulation on one CT-MR brain image,it demonstrats that normalized mutual information method has a good effect.Particularly,it is better than MMI nethod under the condition of lesser overlap region.
Image registration
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Based on the principle of registration of mutual information,this paper discusses that the mutual information algorithm can be used in the geometric alignment of image registration and gives a preliminary result of assessment.By comparison of the results after image registration of CT/MR,PET/MR and available standard,it demonstrates that multimodal medical image registration and sub-pixel registration accuracy can be achieved using the mutual information without any preprocessing steps,which makes this method very well suited for clinical applications.The method is highly reliable and not sensitive to geometrical distortion,intensity inhomogeneity and data missing,and it can avoid obvious errors by adjusting initial parameters properly.
Image registration
Distortion (music)
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Jaccard index
Dice
Sørensen–Dice coefficient
Similarity (geometry)
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Image registration
Robustness
Similarity measure
Medical information
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A measure for registration of medical images that currently draws much attention is mutual information. The measure originates from information theory, but has been shown to be successful for image registration as well. Information theory, however, offers many more measures that may be suitable for image registration. These all measure the divergence of the joint distribution of the images' grey values from the joint distribution that would have been found had the images been completely independent. This paper compares the performance of mutual information as a registration measure with that of other F-information measures. The measures are applied to rigid registration of positron emission tomography (PET)/magnetic resonance (MR) and MR/computed tomography (CT) images, for 35 and 41 image pairs, respectively. An accurate gold standard transformation is available for the images, based on implanted markers. The registration performance, robustness and accuracy of the measures are studied. Some of the measures are shown to perform poorly on all aspects. The majority of measures produces results similar to those of mutual information. An important finding, however, is that several measures, although slightly more difficult to optimize, can potentially yield significantly more accurate results than mutual information.
Image registration
Robustness
Information Theory
Kullback–Leibler divergence
Divergence (linguistics)
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Image registration
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Automatic segmentation of medical images, such as computed tomography (CT) or magnetic resonance imaging (MRI), plays an essential role in efficient clinical diagnosis. While deep learning have gained popularity in academia and industry, more works have to be done to improve the performance for clinical practice. U-Net architecture, along with Dice coefficient optimization, has shown its effectiveness in medical image segmentation. Although it is an efficient measurement of the difference between the ground truth and the network's output, the Dice loss struggles to train with samples that do not contain targeted objects. While the situation is unusual in standard datasets, it is commonly seen in clinical data, where many training data available without the anomalies shown in the images, such as lesions and anatomic structures in some CTs/regions. In this paper, we propose a novel loss function - Stochastic Aggregated Dice Coefficient (SA Dice) and a modification of the network structure to improve its performance. Experimentally, in our own heart aorta CT dataset, our models beats the baseline by 4% in cross-validation Dice scores. In BRATS 2017 brain tumor segmentation challenge, the models also perform better than the state-of-the-art by approximately 2%.
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Sørensen–Dice coefficient
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Registration based on mutual information is a typical method in medical image registration.Mutual information is a common similarity measure in image registration,which has excellent robustness and accuracy,but large calculation amount makes it difficult to be applied to clinics.A maximization of mutual information based image registration method is described.Firstly Because of using maximum mutual information to image registration have inferiority,the registration based on local curvature maximum to obtain corner points.Then,the one to one matching points could be obtained through mutual information rough match.Experimental results indicate the proposed algorithm can achieve better accuracy and good robust.
Image registration
Robustness
Maximization
Similarity measure
Similarity (geometry)
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