Objective: We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. Methods: A variant of the U-Net architecture is used to perform at...
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Objective: We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. Methods: A variant of the U-Net architecture is used to perform atrial segmentation via a deep convolutional neural network, and we compare performance with the kasssnake model. It can be used to determine the surgical success of atrial septal occlusion (ASO) pre- and post- the implantation of the septal occluder, which is based on the volume restoration of the right atria (RA) and left atria (LA). Results: The method was evaluated on a test dataset containing 550 two-dimensional image slices, outperforming conventional active contouring regarding the Dice similarity coefficient, Jaccard index, and Hausdorff distance, and achieving segmentation in the presence of ghost artifacts that occlude the atrium outline. This problem has been unsolvable using traditional machine learning algorithm pertaining to active contouring via the kass snake algorithm. Moreover, the proposed technique is closer to manual segmentation than the snakes active contour model in mean of atrial area (M-AA), mean of atrial maximum diameter (M-AMXD), mean atrial minimum diameter (M-AMID), and mean angle of the atrial long axis (M-AALA). Conclusion: After segmentation, we compute the volume ratio of right to left atria, obtaining a smaller ratio that indicates better restoration. Hence, the proposed technique allows to evaluate the surgical success of atrial septal occlusion and may support diagnosis regarding the accurate evaluation of atrial septal defects before and after occlusion procedures. (C) 2020 Elsevier B.V. All rights reserved.
The Leaf is the important part of the plant which contains important information which will be playing a role in the identification and classification of plants. The identification of plants can be done with the help ...
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ISBN:
(纸本)9781538618882
The Leaf is the important part of the plant which contains important information which will be playing a role in the identification and classification of plants. The identification of plants can be done with the help of image processing techniques. The techniques are used to understand, to analyze, to interpret and to get better quality of the images for human/machine perception. The image processing includes image pre-processing, segmentation, feature extraction and classification. In this paper, the leaf segmentation of different plant such as Jackfruit, Banaba, Cotton and etc. have been experimented using greedy snakealgorithm and it is compared with the M kass snake algorithm. From the comparison, it is observed that greedy algorithm is faster and efficient than the kassalgorithm in terms of iterations obligatory to get the desired contour of an image.
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