Region Of Interest Labeling Research Articles

Auto-ROIs of bone scan image using thin-plate spline with specific bone landmarks

Bone scan image (BSI) is the most widely available technique for detecting cancer that has spread (metastasized) from the original location, such as breast or prostate cancer. One benefit of this process is as strong prognostic indicator of survival longevity for cancer patient. However, some challenging issues should be considered, i.e. variability in image brightness, abnormal bone shape or structure, asymmetry gesture position, and image noise due to muscle and fat tissue make the process to determine the region of interest (ROI) become more difficult, not to mention the required-time when inspecting the ROI of each bone part manually. The purposes of this study are to provide an effortless and meticulous ROI labeling as prerequired in clinical application. Finally, to propose a novel framework for determination of multiple ROI automatically by utilizing specific bone landmarks, in the hope of that could contribute to clinical observation in metastasis analysis. The results show that the proposed framework is promising method with high sensitivity (0.96), specificity (0.97), precision (0.94), Sorensen-Dice Index (0.96) and image content index (0.96).

The Heterogeneity in Retrieved Relations between the Personality Trait 'Harm Avoidance' and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings.

Concerns are raising about the large variability in reported correlations between gray matter morphology and affective personality traits as ‘Harm Avoidance’ (HA). A recent review study (Mincic 2015) stipulated that this variability could come from methodological differences between studies. In order to achieve more robust results by standardizing the data processing procedure, as a first step, we repeatedly analyzed data from healthy females while changing the processing settings (voxel-based morphology (VBM) or region-of-interest (ROI) labeling, smoothing filter width, nuisance parameters included in the regression model, brain atlas and multiple comparisons correction method). The heterogeneity in the obtained results clearly illustrate the dependency of the study outcome to the opted analysis settings. Based on our results and the existing literature, we recommended the use of VBM over ROI labeling for whole brain analyses with a small or intermediate smoothing filter (5-8mm) and a model variable selection step included in the processing procedure. Additionally, it is recommended that ROI labeling should only be used in combination with a clear hypothesis and that authors are encouraged to report their results uncorrected for multiple comparisons as supplementary material to aid review studies.

Abstract T P372: Comparing Methods for Detecting White Matter Abnormalities in Neonates With Hypoxic-Ischemic Encephalopathy

Purpose: To compare manual region of interest (ROI) labeling and tract-based spatial statistics (TBSS) by their ability to detect group-wise differences in fractional anisotropy (FA) in the neonatal brain. Materials and Methods: Diffusion weighted data were obtained for nine infants with hypoxic-ischemic encephalopathy (HIE) (6 males, 3 females; gestational age range, 36-40 weeks; mean gestational age, 37.8 weeks) and eleven healthy-born infants (10 males, 1 female; gestational age range, 36-40 weeks; mean gestational age, 38.4 weeks) on a 3T scanner. For manual ROI labeling, eight ROIs were drawn freehand for each subject. For TBSS, all FA data underwent an optimized, automated protocol for neonates. Each method was evaluated for detection of decreased FA in HIE infants, sensitivity, specificity, and variability. Results: FA values from manual ROI and TBSS were strongly correlated. Both methods found decreased FA in most ROIs for HIE infants. There was no significant interaction between method and group, indicating a similar ability to detect FA differences. Sensitivity (manual: 0.71, TBSS: 0.69), specificity (manual and TBSS: 0.72), and standard error (manual: 0.009, TBSS: 0.007) were comparable. Conclusions: Manual ROI labeling and TBSS are comparable methods of diffusion analysis to detect group differences in FA in the neonatal brain

Automated Segmentation of Mouse Brain Images Using Multi-Atlas Multi-ROI Deformation and Label Fusion

We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.

A novel algorithm for initial lesion detection in ultrasound breast images

This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer‐aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast images to evaluate the performance of the proposed approach. Images were preprocessed using histogram equalization before hybrid filtering and multifractal analysis were conducted. Subsequently, thresholding segmentation was applied on the image. Finally, the initial lesions are detected using a rule‐based approach. The accuracy of the automated ROI labeling was measured as an overlap of 0.4 with the lesion outline as compared with lesions labeled by an expert radiologist. We compared the performance of the proposed method with that of three state‐of‐the‐art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique. We conclude that the proposed method is more accurate and performs more effectively than do the benchmark algorithms considered.PACS numbers: 87.57.Nk