Segmenting an entire 3dimage often has high computational complexity and requires large memory consumption;by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully le...
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ISBN:
(纸本)9781665412469
Segmenting an entire 3dimage often has high computational complexity and requires large memory consumption;by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully leverage the 3ddata. To address this challenge, we propose a multi-dimensional attention network (MdA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network, which results in high segmentation accuracy with a low computational cost. We evaluate our model on the MICCAI iSeg and IBSR datasets, and the experimental results demonstrate consistent improvements over existing methods.
imagesegmentation is the basis of medical image processing. The proposed method can segment 3d volume data by supervoxel and kernel FCM algorithm. Firstly, the extended SLIC method is used to divide the 3dimage into...
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ISBN:
(纸本)9781450376570
imagesegmentation is the basis of medical image processing. The proposed method can segment 3d volume data by supervoxel and kernel FCM algorithm. Firstly, the extended SLIC method is used to divide the 3dimage into supervoxels. Then the supervoxels are descripted by statistical feature and segmented by kernel FCM algorithm. The experimental results on a publically available brain MR dataset show the effectiveness and superior performance of the proposed method.
3d image segmentation is a fundamental process in many scientific and medical applications. Automatic algorithms do exist, but there are many use cases where these algorithms fail. The gold standard is still manual se...
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ISBN:
(纸本)9781450341257
3d image segmentation is a fundamental process in many scientific and medical applications. Automatic algorithms do exist, but there are many use cases where these algorithms fail. The gold standard is still manual segmentation or review. Unfortunately, even for an expert this is laborious, time consuming, and prone to errors. Existing 3dsegmentation tools do not currently take into account human mental models and low-level perception tasks. Our goal is to improve the quality and efficiency of manual segmentation and review by analyzing how experts perform segmentation. As a preliminary step we conducted a field study with 8 segmentation experts, recording video and eye tracking data. We developed a novel coding scheme to analyze this data and verified that it successfully covers and quantifies the low-level actions, tasks and behaviors of experts during 3d image segmentation.
segmentation of an ultrasoundimage into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues...
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segmentation of an ultrasoundimage into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. differences and inconsistencies in ultrasound interpretation call for an automatedsegmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3d) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3d ultrasound volumes into three major tissue types: cyst /mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentationdemonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3d whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. (C) 2015 Elsevier B.V. All rights reserved.
In the domain of three-dimensional (3d) particle holography, diverse imaging characteristics across particles of varying scales and positions pose a notable impediment to efficient information extraction. This researc...
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In the domain of three-dimensional (3d) particle holography, diverse imaging characteristics across particles of varying scales and positions pose a notable impediment to efficient information extraction. This research introduces an adaptive detection andsegmentation approach employing mechanism-guided machine learning techniques. Not only the carefully chosen detection network but also the cropping pretreatment of input images are used to obtain the precise regions of interest (ROIs) of particles, inspired with the estimated particle size distribution under specific experimental conditions. Restricted by the blurred particle boundary and aiming for a robust method for hyperparameter, cluster-basedsegmentation is proposed with an extra feature map, which takes the unique change of object signals across different slices compared with background into account. Rigorous evaluation of different detection network configurations, training data, andsegmentation methods are conducted on experimental data from an icing wind tunnel and calibration dot board. Concrete ablation experiments and analysis of the mechanism-guided operations (cropping and the introduction of the extra feature map) are provided. The influence of the hyperparameter in segmentation is also illustrated. To showcase its robustness, the proposed method is applied to segment a swirl spray particle field, demonstrating its versatility and applicability across varied particle field scenarios. The code anddata for the paper are available at https://github .com /Adiazhang0426 /HOLOAI /tree /master and https://drive .google .com /drive / folders /1C9QlaklXpApIpE8AukCNMTKUnZw _YcGJ ?usp =drive _link.
segmentation of an ultrasoundimage into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues...
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ISBN:
(纸本)9781510600256
segmentation of an ultrasoundimage into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. differences and inconsistencies in ultrasound interpretation call for an automatedsegmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3d) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3d ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentationdemonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3d whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.
To enhance the diagnosis and treatment of brain tumors, precise segmentation of the entire tumor region is crucial. Medical image fusion is a method that integrates complementary information from different modal image...
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ISBN:
(纸本)9798350359329;9798350359312
To enhance the diagnosis and treatment of brain tumors, precise segmentation of the entire tumor region is crucial. Medical image fusion is a method that integrates complementary information from different modal images into a synthetically enrichedimage. In current research, 2d slice fusion methods are prevalent, but they often lose essential spatial context information when fusing volumetric structures in medical images. Furthermore, existing 3d medical image fusion techniques fail to fully exploit the features of the source modality, leading to the loss of crucial modality information. Addressing these challenges, this paper proposes an Average Recalibration Fu- sion segmentation Network (ARS-Net) for the task of 3d multi-modal brain tumor segmentation. The network comprises two-stage feature fusion: a fusion module based on simple averaging and a feature recalibration module aimed at maximizing the utilization of learned neural network features. By appropriately integrating spatial information from the features, we can maintain the relative priority of activated regions, thereby facilitating channel recalibration. Additionally, we design a novel loss function specifically considering the features of different MRI modalities to better preserve modality information. In a series of experiments, our methoddemonstrates more competitive performance in both visual quality and objective evaluation compared to other representative 3d and 2d medical image fusion methods. Further experiments validate the effectiveness of the proposed method in brain tumor segmentation, showing a significant improvement in segmentation accuracy.
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Automatic volume-of-interest segmentation based on multi-parametric MR imaging is crucial for computer-aideddisease diagnosis, ...
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ISBN:
(纸本)9781665473583
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Automatic volume-of-interest segmentation based on multi-parametric MR imaging is crucial for computer-aideddisease diagnosis, treatment planning, and prognosis monitoring. despite the extensive studies conducted in deep learning-based medical image analysis, further investigations are still required to effectively exploit the information provided by different imaging parameters. How to fuse the information is a key question in this field. Here, we propose an uncertainty-aware multi-parametric MR image feature fusion method to fully exploit the information for enhanced3d image segmentation. Uncertainties in the independent predictions of individual modalities are utilized to guide the fusion of multi-modal image features. Extensive experiments on two datasets, one for brain tissue segmentation and the other for abdominal multi-organ segmentation, have been conducted, and our proposed method achieves better segmentation performance when compared to existing models.
despite continuing advances in mathematical models for automatic segmentation, many medical practitioners still rely on 2d manual delineation, due to the lack of intuitive automatic tools in 3d. In this paper, an effi...
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despite continuing advances in mathematical models for automatic segmentation, many medical practitioners still rely on 2d manual delineation, due to the lack of intuitive automatic tools in 3d. In this paper, an efficient 3d medical imagesegmentation technique is proposed to provide 3d representation of the segmented regions. It uses graph cut and contour filling algorithms. It uses the normalised cut method with the eigenvector of the second smallest eigenvalue to solve the imagesegmentation problem, and the contour filling algorithm to ensure that the segmented region is free of gap and hole artefacts. The experimental results reveal that the proposed technique can provide a 3d representation of the region of interest successfully. The segmentations produced by this method are more realistic than the previously proposedsegmentation techniques besides its effectiveness in reducing the amount of gaps and holes.
Industrial Computed Tomography (CT) serves as a pivotal non-destructive testing technique, significantly impacting the research and application domains of automatic defect detection in three-dimensional (3d) CT images...
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Industrial Computed Tomography (CT) serves as a pivotal non-destructive testing technique, significantly impacting the research and application domains of automatic defect detection in three-dimensional (3d) CT images. Recent advancements have showcased the exceptional capabilities of Convolutional Neural Networks (CNNs) in addressing image recognition andsegmentation tasks. In the context of industrial 3d CT imaging, the paramount challenge lies in the segmentation of microstructural cracks & acirc;task complicated by low grayscale contrast, small sizes, and imbalanced sample distributions. To tackle these complexities, we introduce a novel crack segmentation methodology grounded in multi-task deep convolutional neural *** network architecture is composed of dual modules: a segmentation task module dedicated to crack extraction and a classification task module aimed at enhancing accuracy, with shared feature maps to leverage synergistic benefits. Inspired by the work of Chen et al., we incorporate the Efficient Channel Attention (ECA) block, an attention mechanism designed to refine segmentation accuracy. Empirical evaluations demonstrate that our proposed approach outperforms existing methodologies, showcasing superior performance in crack segmentation tasks. Notably, our network exhibits commendable generalization abilities. despite being trained exclusively on simulatedimages, it yields robust and accurate predictions when applied to real-world CT images, indicating its adaptability across varieddatasets. This study not only advances the technological frontier in industrial CT image analysis but also furnishes a potent tool and reference for future research endeavors in related fields.
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