Segmentation Algorithm For MR Images Research Articles

Multi-resolution dual-encoder self-constrained brain tumor MR image segmentation algorithm

Multi-resolution dual-encoder self-constrained brain tumor MR image segmentation algorithm

Multi-resolution dual-encoder self-constrained brain tumour MR image segmentation algorithm

Multi-resolution dual-encoder self-constrained brain tumour MR image segmentation algorithm

A New Hybrid Brain MR Image Segmentation Algorithm With Super-Resolution, Spatial Constraint-Based Clustering and Fine Tuning

Tissue segmentation from a single brain MR image is of paramount importance for brain reconstruction and analysis. In this paper, we propose a new hybrid algorithm for brain MR image segmentation, combining super-resolution, spatial constraint based clustering and fine-tuning. To smooth noise and improve image clarity, we first amplify the brain MR image by using a super-resolution algorithm - cubic surface fitting with edges in the image as constraints. Then an improved fuzzy c-means clustering algorithm is performed on the amplified image for the global segmentation, in which a shape parameter and an anomaly detection parameter are introduced. With the introduction of these two parameters, the robustness of the clustering is enhanced, and the trade-off between noise smoothing and detail preservation can be controlled more accurately. Furthermore, the local regions around boundaries of different brain tissues (e.g., gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)) are re-segmented in a fine-tuning process, and a soft voting strategy is adopted for adjusting the incorrect pixels, which makes full use of the boundary details of different tissues. Experimental results show the new algorithm can preserve major brain tissue structures and smooth out noise.

An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis.

Existing brain region segmentation algorithms based on deep convolutional neural networks (CNN) are inefficient for object boundary segmentation. In order to enhance the segmentation accuracy of brain tissue, this paper proposed an object region segmentation algorithm that combines pixel-level information and semantic information. Firstly, we extract semantic information by CNN with the attention module and get the coarse segmentation results through a specific pixel-level classifier. Then, we exploit conditional random fields to model the relationship between the underlying pixels so as to get local features. Finally, the semantic information and the local pixel-level information are respectively used as the unary potential and the binary potential of the Gibbs distribution, and the combination of both can obtain the fine region segmentation algorithm based on the fusion of pixel-level information and the semantic information. A large number of qualitative and quantitative test results show that our proposed algorithm has higher precision than the existing state-of-the-art deep feature models, which can better solve the problem of rough edge segmentation and produce good 3D visualization effect.

Automatic tissue segmentation of head and neck MR images for hyperthermia treatment planning

A hyperthermia treatment requires accurate, patient-specific treatment planning. This planning is based on 3D anatomical models which are generally derived from computed tomography. Because of its superior soft tissue contrast, magnetic resonance imaging (MRI) information can be introduced to improve the quality of these 3D patient models and therefore the treatment planning itself. Thus, we present here an automatic atlas-based segmentation algorithm for MR images of the head and neck.Our method combines multiatlas local weighting fusion with intensity modelling. The accuracy of the method was evaluated using a leave-one-out cross validation experiment over a set of 11 patients for which manual delineation were available.The accuracy of the proposed method was high both in terms of the Dice similarity coefficient (DSC) and the 95th percentile Hausdorff surface distance (HSD) with median DSC higher than 0.8 for all tissues except sclera. For all tissues, except the spine tissues, the accuracy was approaching the interobserver agreement/variability both in terms of DSC and HSD. The positive effect of adding the intensity modelling to the multiatlas fusion decreased when a more accurate atlas fusion method was used.Using the proposed approach we improved the performance of the approach previously presented for H&N hyperthermia treatment planning, making the method suitable for clinical application.

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method. The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

Emerging inhaled bronchodilators: an update

Bronchodilators remain central to the symptomatic management of chronic obstructive pulmonary disease and asthma, and, for this reason and also because the patent protection of many bronchodilators has expired, several companies have reinitiated research into the field. The only limits set for the development of a long-lasting bronchodilator with a new product profile are medical needs and marketing opportunities. The incorporation of once-daily dose administration is an important strategy for improving adherence and is a regimen preferred by most patients. A variety of beta(2)-agonists and antimuscarinic agents with longer half-lives and inhalers containing a combination of several classes of long-acting bronchodilator are currently under development. The present article reviews all of the most important compounds under development, describing what has been done and discussing their genuine advantage.