Introduction to Label Fusion

What is/are Label Fusion?

Label Fusion - We compare our method with the label fusion of 13 organs on state-of-the-art Deeds registration method and achieved Dice score of 92. ^[1] Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. ^[2] In this paper, multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods, conventional methods for label fusion, datasets that have been used for evaluating the multi-atlas methods, as well as the applications of multi-atlas based segmentation in clinical researches. ^[3] Choosing well‐registered atlases for label fusion is vital for an accurate segmentation. ^[4] Patch-based label fusion in the target space has shown to produce very accurate segmentations although at the expense of registering all atlases to each target image. ^[5] METHODS Our approach dynamically selects and weights the appropriate number of atlases for weighted-label fusion and generates segmentations and consensus maps indicating voxel-wise agreement between different atlases. ^[6] Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. ^[7] After the label fusion with majority voting, we finally constructed a 3D-FCN to further refine the boundary voxels with low voting values. ^[8] Label fusion is one of the key steps in multi-atlas based segmentation of structural magnetic resonance (MR) images. ^[9] In the single robot semantic mapping process, Bayesian rule is used for label fusion and occupancy probability updating, where the semantic information is added to the geometric map grid. ^[10] In label fusion, the coefficient as a specific weight is assigned to target label image based on the correlation function between atlases. ^[11] We incorporate neighborhood information to label fusion so that final label estimation is more accurate and robust for diseased hips with joint space narrowing. ^[12]

multi atlas segmentation

To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. ^[1] Background Label fusion is a core step of Multi-Atlas Segmentation (MAS), which has a decisive effect on segmentation results. ^[2] In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. ^[3] Label propagation and label fusion using multiple atlases have made multi-atlas segmentation approach as forefront of segmentation research. ^[4]

multi atlas joint

Compared to several existing state-of-the-art segmentation methods for subcortical structures, including a multi-atlas joint label fusion method and a representative 3D FCN method, the proposed method performed significantly better for a majority of the subcortical structures. ^[1] Compared to several existing state-of-the-art segmentation methods including a multi-atlas joint label fusion method and three representative fully convolutional network methods, the proposed method performed significantly better for a majority of the 12 subcortical structures, with the overall mean Dice scores being respective 0. ^[2] The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. ^[3]

Joint Label Fusion

Specifically, we extend the joint label fusion method by taking model uncertainty into account when estimating correlations among predictions produced by different modalities. ^[1] We first report a machine learning framework for brain tumor growth modeling, tumor segmentation and tracking in longitudinal mMRI scans, comprising of two methods: feature fusion and joint label fusion (JLF). ^[2] Compared to several existing state-of-the-art segmentation methods for subcortical structures, including a multi-atlas joint label fusion method and a representative 3D FCN method, the proposed method performed significantly better for a majority of the subcortical structures. ^[3] spatially varying weighted voting and joint label fusion, in the context of segmenting medial temporal lobe subregions in T1-weighted MRI. ^[4] Compared to several existing state-of-the-art segmentation methods including a multi-atlas joint label fusion method and three representative fully convolutional network methods, the proposed method performed significantly better for a majority of the 12 subcortical structures, with the overall mean Dice scores being respective 0. ^[5] The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. ^[6] Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0. ^[7] By comparing with the non-reinforced segmentation and a classical multi-atlas method with joint label fusion, the proposed approach obtains better results. ^[8] When using the same 45 training images, AssemblyNet outperforms global U-Net by 28% in terms of the Dice metric, patch-based joint label fusion by 15% and SLANT-27 by 10%. ^[9]

Atla Label Fusion

The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. ^[1] We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. ^[2] The T1-weighted images were automatically parcellated for hippocampus and amygdala, as well as the intracranial volume (ICV), total brain volume, total gray and white matter, using a multi-atlas label fusion method implemented in the MRICloud ( https://braingps. ^[3] We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. ^[4] We also compare AdaPro with three other state-of- the-art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi-atlas label fusion. ^[5] The multi‐atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. ^[6] DISCUSSION Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. ^[7] After multi-atlas label fusion by majority voting, we possess noisy labels for each of the targeted LGE images. ^[8]

label fusion method

In this paper, we propose a robust discriminative label fusion method under the multi-atlas framework. ^[1] The T1-weighted images were automatically parcellated for hippocampus and amygdala, as well as the intracranial volume (ICV), total brain volume, total gray and white matter, using a multi-atlas label fusion method implemented in the MRICloud ( https://braingps. ^[2] Specifically, we extend the joint label fusion method by taking model uncertainty into account when estimating correlations among predictions produced by different modalities. ^[3] In this work, a multi-atlas patch-based label fusion method is presented for automatic brain extraction from neonatal head MR images. ^[4] Compared to several existing state-of-the-art segmentation methods for subcortical structures, including a multi-atlas joint label fusion method and a representative 3D FCN method, the proposed method performed significantly better for a majority of the subcortical structures. ^[5] Compared to several existing state-of-the-art segmentation methods including a multi-atlas joint label fusion method and three representative fully convolutional network methods, the proposed method performed significantly better for a majority of the 12 subcortical structures, with the overall mean Dice scores being respective 0. ^[6] The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. ^[7] Finally, hard labels of multiple attributes are adaptively fused into a soft label by the proposed multi-label fusion method based on the idea of Bayesian inference, which makes the attribute labels suitable for regression tasks. ^[8] However, a precise segmentation of brain subcortical structures in a magnetic resonance image is still difficult since (1) brain MRI typically suffers low tissue contrast; and (2) image patterns around the boundary of a structure are similar such that similarity-based and reconstruction-based label fusion methods achieve inaccurate results. ^[9] The model was evaluated on a set of cardiac CTA images with comparison to related shape prior and local region-based methods and multi-atlas joint label fusion methods, and experimental results show it achieves competitive accuracies of segmenting myocardial epicardium and endocardium parts. ^[10]

label fusion approach

Finally, we develop a label fusion approach to make a final classification decision for new testing samples. ^[1] Finally, we develop a label fusion approach to make a final classification decision for new testing samples. ^[2] Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0. ^[3] The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the registration algorithm and the label fusion approach, if more than one atlas is employed. ^[4]

label fusion strategy

We first review various existing patch-based multiatlas label fusion strategies. ^[1] We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. ^[2] In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. ^[3]

label fusion technique

We then conduct an empirical study on their cost-effectiveness, showing that the performance of the existing active learning approaches is affected by many factors in hybrid classification contexts, such as the noise level of data, label fusion technique used, and the specific characteristics of the task. ^[1] We extend the graphical model used in label fusion techniques for the segmentation of multi-modality Magnetic Resonance brain images. ^[2] Additionally, the set of labels are merged using a label fusion technique that reduces the errors produced by the registration. ^[3]

label fusion term

Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. ^[1] We define a new energy functional by combining a weighted label fusion term, a bias field based image information fitting term and a regularization term together. ^[2]