A texture analysis method for MR images of airway dilator muscles: a feasibility study.
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ABSTRACT:
SUBMITTER: Kolhi P
PROVIDER: S-EPMC4082267 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:BackgroundOur objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA).MethodsWe imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters.ResultsThere were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls.ConclusionsTA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.
| S-EPMC3161385 | biostudies-literature
Project description:PurposeTo assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging.Materials and methodsFrom April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.ResultsBased on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.ConclusionTexture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.
| S-EPMC4351185 | biostudies-literature
Project description:PurposeTo investigate the ability of qualitative Magnetic Resonance (MR) images features and quantitative Magnetic Resonance Imaging (MRI) texture features in the contrastive analysis between craniopharyngioma and meningioma.MethodA total number of 127 patients were included in this study (craniopharyngioma = 63; meningioma = 64). All the features analyzed in this study were acquired from preoperative MRI images. Qualitative MR images features were evaluated with chi-square tests or Fisher exact test, while MRI texture features were evaluated with the Mann-Whitney U test with the Benjamini-Hochberg method. Then binary logistic regression analysis for texture features was performed to evaluate their ability as independent predictors, and the diagnostic accuracy was calculated next for these texture features with high abilities as independent predictors using receiver operating characteristic (ROC) curves.ResultsFour qualitative MR images features showed significant difference between craniopharyngioma and meningioma, but only cystic alteration could be considered as diagnostic independent predictors. Meanwhile, three quantitative parameters, histogram-based matrix- (HISTO-) Skewness, Grey-level co-occurrence matrix- (GLCM-) Contrast on contrast-enhanced images, and HISTO-Skewness on images of T2-weighted imaging (T2WI), showed promising abilities in the contrastive analysis. Besides, these texture features were found significantly to be relative to cystic alteration.ConclusionMR images features and texture features were useful in the contrastive analysis of craniopharyngioma and meningioma. Furthermore, qualitative MR images features and MRI texture features could be related to each other.
| S-EPMC7049426 | biostudies-literature
Project description:Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images.A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 dataset.The mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 dataset, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images.The proposed supervoxel-based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy.
| S-EPMC5540668 | biostudies-literature
Project description:PURPOSE:To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). METHODS:This retrospective study included 41 women (ALM?=?22, LMS?=?19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM?=?14, LMS?=?10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. RESULTS:Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p???0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ?3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). CONCLUSIONS:Combination of ?3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. KEY POINTS:• Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ?3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.
| S-EPMC5459669 | biostudies-literature
Project description:Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.
| S-EPMC7964174 | biostudies-literature
Project description:This study evaluated the feasibility of using only diagnostically relevant magnetic resonance (MR) images together with deep learning for positron emission tomography (PET)/MR attenuation correction (deepMRAC) in the pelvis. Such an approach could eliminate dedicated MRAC sequences that have limited diagnostic utility but can substantially lengthen acquisition times for multibed position scans. We used axial T2 and T1 LAVA Flex magnetic resonance imaging images that were acquired for diagnostic purposes as inputs to a 3D deep convolutional neural network. The network was trained to produce a discretized (air, water, fat, and bone) substitute computed tomography (CT) (CTsub). Discretized (CTref-discrete) and continuously valued (CTref) reference CT images were created to serve as ground truth for network training and attenuation correction, respectively. Training was performed with data from 12 subjects. CTsub, CTref, and the system MRAC were used for PET/MR attenuation correction, and quantitative PET values of the resulting images were compared in 6 test subjects. Overall, the network produced CTsub with Dice coefficients of 0.79 ± 0.03 for cortical bone, 0.98 ± 0.01 for soft tissue (fat: 0.94 ± 0.0; water: 0.88 ± 0.02), and 0.49 ± 0.17 for bowel gas when compared with CTref-discrete. The root mean square error of the whole PET image was 4.9% by using deepMRAC and 11.6% by using the system MRAC. In evaluating 16 soft tissue lesions, the distribution of errors for maximum standardized uptake value was significantly narrower using deepMRAC (-1.0% ± 1.3%) than using system MRAC method (0.0% ± 6.4%) according to the Brown-Forsy the test (P < .05). These results indicate that improved PET/MR attenuation correction can be achieved in the pelvis using only diagnostically relevant MR images.
| S-EPMC6173790 | biostudies-literature
Project description:The purpose of this study was to apply texture analysis (TA) to evaluate the uniformity of SPECT images reconstructed with the 3D Ordered Subsets Expectation Maximization (3D-OSEM) algorithm. For this purpose, a cylindrical homogeneous phantom filled with 177Lu was used and a total of 24 spherical volumes of interest (VOIs) were considered inside the phantom. The location of the VOIs was chosen in order to define two different configurations, i.e. gravity and radial configuration. The former configuration was used to investigate the uniformity of distribution of 177Lu inside the phantom, while the latter configuration was used to investigate the lack of uniformity from center towards edge of the images. For each VOI, the trend of different texture features considered as a function of 3D-OSEM updates was investigated in order to evaluate the influence of reconstruction parameters. TA was performed using CGITA software. The equality of the average texture feature trends in both spatial configurations was assumed as the null hypothesis and was tested by functional analysis of variance (fANOVA). With regard to the gravity configuration, no texture feature rejected the null hypothesis when the number of subsets increased. For the radial configuration, the statistical analysis revealed that, depending on the 3D-OSEM parameters used, a few texture features were capable of detecting the non-uniformity of 177Lu distribution inside the phantom moving from the center of the image towards its edge. Finally, cross-correlation coefficients were calculated to better identify the features that could play an important role in assessing quality assurance procedures performed on SPECT systems.
| S-EPMC6668785 | biostudies-literature
Project description:PurposeTo evaluate whether deep neural networks trained on a similar number of images to that required during physician training in the American College of Cardiology Core Cardiovascular Training Statement can acquire the capability to detect and classify myocardial delayed enhancement (MDE) patterns.Materials and methodsThe authors retrospectively evaluated 1995 MDE images for training and validation of a deep neural network. Images were from 200 consecutive patients who underwent cardiovascular MRI and were obtained from the institutional database. Experienced cardiac MR image readers classified the images as showing the following MDE patterns: no pattern, epicardial enhancement, subendocardial enhancement, midwall enhancement, focal enhancement, transmural enhancement, and nondiagnostic. Data were divided into training and validation datasets by using a fourfold cross-validation method. Three untrained deep neural network architectures using the convolutional neural network (CNN) technique were trained with the training dataset images. The detection and classification accuracies of the trained CNNs were calculated with validation data.ResultsThe 1995 MDE images were classified by human readers as follows: no pattern, 926; epicardial enhancement, 91; subendocardial enhancement, 458; midwall enhancement, 118; focal enhancement, 141; transmural enhancement, 190; and nondiagnostic, 71. GoogLeNet, AlexNet, and ResNet-152 CNNs demonstrated accuracies of 79.5% (1592 of 1995 images), 78.9% (1574 of 1995 images), and 82.1% (1637 of 1995 images), respectively.ConclusionDeep learning with CNNs using a limited amount of training data, less than that required during physician training, achieved high diagnostic performance in the detection of MDE on MR images.© RSNA, 2019Supplemental material is available for this article.
| S-EPMC8017383 | biostudies-literature
Project description:PurposeMost radiomic studies use the features extracted from the manually drawn tumor contours for classification or survival prediction. However, large interobserver segmentation variations lead to inconsistent features and hence introduce more challenges in constructing robust prediction models. Here, we proposed an automatic workflow for glioblastoma (GBM) survival prediction based on multimodal magnetic resonance (MR) images.Methods and materialsTwo hundred eighty-five patients with glioma (210 GBM, 75 low-grade glioma) were included. One hundred sixty-three of the patients with GBM had overall survival data. Every patient had 4 preoperative MR images and manually drawn tumor contours. A 3-dimensional convolutional neural network, VGG-Seg, was trained and validated using 122 patients with glioma for automatic GBM segmentation. The trained VGG-Seg was applied to the remaining 163 patients with GBM to generate their autosegmented tumor contours. The handcrafted and deep learning (DL)-based radiomic features were extracted from the autosegmented contours using explicitly designed algorithms and a pretrained convolutional neural network, respectively. One hundred sixty-three patients with GBM were randomly split into training (n = 122) and testing (n = 41) sets for survival analysis. Cox regression models were trained to construct the handcrafted and DL-based signatures. The prognostic powers of the 2 signatures were evaluated and compared.ResultsThe VGG-Seg achieved a mean Dice coefficient of 0.86 across 163 patients with GBM for GBM segmentation. The handcrafted signature achieved a C-index of 0.64 (95% confidence interval, 0.55-0.73), whereas the DL-based signature achieved a C-index of 0.67 (95% confidence interval, 0.57-0.77). Unlike the handcrafted signature, the DL-based signature successfully stratified testing patients into 2 prognostically distinct groups.ConclusionsThe VGG-Seg generated accurate GBM contours from 4 MR images. The DL-based signature achieved a numerically higher C-index than the handcrafted signature and significant patient stratification. The proposed automatic workflow demonstrated the potential of improving patient stratification and survival prediction in patients with GBM.
| S-EPMC8377554 | biostudies-literature