User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich, Paul A.; Piven, Joseph; Hazlett, Heather C.; Smith, Rachel G.; Ho, Sean; Gee, James C.; Gerig, Guido

doi:10.1016/j.neuroimage.2006.01.015

Cited by 7,156 publications

(5,206 citation statements)

References 31 publications

(36 reference statements)

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“…In addition to graphical‐based methods, we also explored the performance of a boundary‐based method called the geodesic active contours, which was first introduced by Caselles et al60 We implemented this approach using an open source software called ITK‐SNAP 61. A major challenge of such method is to set several equation parameters, especially the speed function.…”

Section: Discussionmentioning

confidence: 99%

“…Threshold‐based method, as the name implies, utilize the intensities probabilities based on the intensity histogram of the image. For more information calculating the probabilities for this method, the reader is encouraged to read Zhu et al62 and Yushkevich et al61 A major disadvantage of thresholding method is that the intensity histogram does not provide spatial information about the ROIs. Also, there is no consensus on the selection of an optimum threshold level because of the large variability of pathologies, low resolution, inherent noise, and high uncertainties in fuzzy object boundaries 31.…”

Section: Discussionmentioning

confidence: 99%

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Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

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Site‐specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18Flourine–fluorodeoxyglucose (18F‐FDG) PET images for three parameters: manual versus computer‐aided segmentation methods, gray‐level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board‐certified radiation oncologists manually segmented the metabolic tumor volume (MTV1 and MTV2) for each patient. For comparison, we used a graphical‐based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down‐sampled the tumor volumes into three gray‐levels: 32, 64, and 128 from the original gray‐level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D‐reconstruction algorithms: maximum likelihood‐ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning‐ML‐OSEM (FOREIR), FORE‐filtered back projection (FOREFBP), and 3D‐Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray‐levels of down‐sampled volumes, and PET reconstruction algorithms. The features were extracted using gray‐level co‐occurrence matrices (GLCM), gray‐level size zone matrices (GLSZM), gray‐level run‐length matrices (GLRLM), neighborhood gray‐tone difference matrices (NGTDM), shape‐based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV1‐MTV2, MTV1‐GBSV, MTV2‐GBSV; gray‐levels: 64‐32, 64‐128, and 64‐256; reconstruction algorithms: OSEM‐FORE‐OSEM, OSEM‐FOREFBP, and OSEM‐3DRP). We used false|normald¯false| as a measure of radiomic feature reproducibility level, where any feature scored false|normald¯false| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland–Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High— ±1% ≤ U/LRL ≤ ±30%; Intermediate— ±30% < U/LRL ≤ ±45%; Low— ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic fe...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Altazi

Zhang

Fernandez

et al. 2017

J Applied Clin Med Phys

View full text Add to dashboard Cite

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“…The MR image protocol included 51 transversal, 7 coronal, and 2 sagittal spin‐echo T1‐weighted images (section thickness, 5 mm; TR = 24 msec; TE = 9 msec). Manual delineation and segmentation of brainstem structures and the cerebellum in pseudonymized images were performed by SM, who is highly experienced in manual volumetric techniques,19 using the open‐source software ITK‐SNAP version 2.4.0 20. The following volumes were measured: the total cerebellum, the total brainstem.…”

Section: Methodsmentioning

confidence: 99%

Brain atrophy measures in preclinical and manifest spinocerebellar ataxia type 2

Reetz

Rodríguez‐Labrada²,

Dogan

et al. 2018

Ann Clin Transl Neurol

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ObjectiveSpinocerebellar ataxia type 2 (SCA2) is an autosomal dominantly inherited neurodegenerative disease mainly affecting the cerebellum and brainstem. In this Cuban‐German research collaboration, we aimed to characterize atrophy patterns and associations with clinical measures in preclinical and manifest SCA2.MethodsIn this study, 16 nonmanifest SCA2 mutation carriers, 26 manifest patients with SCA2, and 18 healthy control subjects underwent magnetic resonance imaging, as well as genetic and clinical characterization including assessment of ataxia (Scale for the Assessment and Rating of Ataxia) and saccade velocity in Cuba were enrolled. Semiautomated quantitative volumetry of the cerebellum and brainstem, subdivided into the medulla oblongata, the pontine brainstem, and mesencephalon was performed. Additionally, the anteroposterior diameter of the pontine brainstem was measured.ResultsAnalysis of volumetric data revealed degeneration of the cerebellum and brainstem, in particular of pontine volumes and the anteroposterior diameter of the pons, in both manifest SCA2 patients and individuals at risk for SCA2 compared to controls. Comparing patients with nonataxic preclinical SCA2 mutation carriers, we found more pronounced reductions of the pontine brainstem and cerebellum in manifest SCA2. Volumetric data further showed associations with CAG repeat length and predicted age of onset in preclinical SCA2 individuals, and by trend with ataxia signs in patients. Although saccade velocity was associated with reduction in the pontine brainstem in preclinical and manifest SCA2, reduced ability to suppress interfering stimuli measured by the Stroop task was related to cerebellar volume loss in patients.InterpretationPreclinical SCA2 mutation carriers exhibit brain abnormalities, which could be targeted as surrogate parameters for disease progression and in future preventive trials.

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“…Focal lesions included any cystic cavities and other focal regions of abnormal signal in the white or gray matter. The ITK-SN AP software (Yushkevich et al, 2006; http://www.itk.org) was used for a 3D -based segmentation. Total lesion volume was calculated using a stand-alone utility provided by VoxBo software (Center for Functional Neuroimaging, Philadelphia, PA, http://www.voxbo.org).…”

Section: Lesion Assessmentmentioning

confidence: 99%

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

et al. 2008

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Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid-and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/ temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume> 1.5 cm 3 ). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain-behavior relationships in the TBI population.

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User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Cited by 7,156 publications

References 31 publications

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Reproducibility of F18‐FDG PET radiomic features for different cervical tumor segmentation methods, gray‐level discretization, and reconstruction algorithms

Brain atrophy measures in preclinical and manifest spinocerebellar ataxia type 2

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

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