Cartilage volume quantification via Live Wire segmentation

RATIONALE AND OBJECTIVES: A reduction in cartilage volume is characteristic of osteoarthritis and hence there exists a need for an accurate and reproducible method to measure in vivo cartilage volume. Quantification of cartilage volume from magnetic resonance (MR) images requires a segmentation technique such as the user-driven "Live Wire" strategy that can reliably delineate object volumes in a time-efficient manner. In the present work, the accuracy and reproducibility of the Live Wire method for the quantification of cartilage volume in MR images is evaluated. MATERIALS AND METHODS: The accuracy of the Live Wire method was assessed by comparing the MR-based volume measurement of a patellar cartilage-shaped phantom versus data calculated via water displacement. The inter- and intra-operator reproducibility of the technique was evaluated from Live Wire segmentation of the patellar cartilage volume from fat-suppressed 3-dimensional spoiled-gradient-echo images of five healthy human volunteers performed by three operators. To provide data for analysis of inter-scan reproducibility, the human scans were repeated five times with the aid of a leg-restraining jig to minimize repositioning error. RESULTS: The volume of the patellar cartilage-shaped phantom measured via Live Wire segmentation of MR images was within 97.8% of its true volume. The average inter- and intra-operator coefficients of variation of three operators were 3.0% and 0.4%, respectively. The average inter-scan coefficient of variation of five repeated scans of each volunteer was 2.7%. CONCLUSION: The data suggest that the Live Wire strategy is an accurate, reproducible, and efficient technique to measure cartilage volume in vivo in a feasible amount of operator time.     

Reproducibility of intracranial volume measurement by unsupervised multispectral brain segmentation

To assess the inter-study variability of a recently published unsupervised segmentation method (Magn. Reson. Med. 1997;37:84–93), 14 brain MR studies were performed in five normal subjects. Standard deviations for absolute and fractional volumes of intracranial compartments, which reflect the experimental variability, were smaller than 16.5 ml and 1.1%, respectively. By comparing the experimental component of the variability with the variability observed in our reference database, an estimate of the biological variability of the intracranial fractional volumes in the database population was obtained.     

Stereological estimation of total intracranial volume on CT images

This study aims to investigate the possibility of generating stereological estimations of total intracranial volume (TIV) on CT scans. The study group included 16 consecutive patients referred for a cranial CT examination. The TIV was estimated using the stereological point counting technique. Volume measurements were optimized by systematically sampling CT sections and by defining an optimum spacing between test points of the grid. The intraobserver and interobserver variability of the optimized volumetric technique was determined. Stereological TIV estimations were compared with the respective planimetric measurements. The application of a test grid with a point spacing of 2.4 cm on 6–8 systematically sampled CT sections provided TIV estimations with a coefficient of error of less than 5%. The intraobserver and interobserver coefficient of variation values were found to be 2.4 and 4.0%, respectively. The 95% limits of agreement between stereological and planimetric TIV measurements were equal to –91.4 and 103.4 cm³. The mean time (±SD) needed to obtain stereological TIV estimations was 2.9±0.6 min. The application of the optimized stereological technique on CT scans enables the efficient estimation of TIV.     

Automated measurement of single and total lung volume from CT.

PURPOSE: The goal of this work was to develop an automated method for calculating single (SLV) and total (TLV) lung volumes from CT images. METHOD: Patients underwent volumetric CT scanning through the entire chest in a single breath-hold, as well as pulmonary function tests. An automated, knowledge-based system was developed to segment the lungs in the CT images. Image-processing routines were used to extract sets of voxels from the image data that were identified by matching them to anatomical objects defined in a model. SLV and TLV were calculated by summing included voxels. RESULTS: For 43 patients analyzed, TLV from CT and total lung capacity from body plethysmography were strongly correlated (r = 0.90). On average, the CT-derived volume of the left lung accounted for 47.2% of the total. CONCLUSION: A knowledge-based approach to segmentation of the lungs in CT can be used to automatically estimate SLV and TLV.     

Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis

A fully automated magnetic resonance (MR) segmentation method for identification and volume measurement of demyelinated white matter has been developed. Spin-echo MR brain scans were performed in 38 patients with multiple sclerosis (MS) and in 46 healthy subjects. Segmentation of normal tissues and white matter lesions (WML) was obtained, based on their relaxation rates and proton density maps. For WML identification, additional criteria included three-dimensional (3D) lesion shape and surrounding tissue composition. Segmented images were generated, and normal brain tissues and WML volumes were obtained. Sensitivity, specificity, and reproducibility of the method were calculated, using the WML identified by two neuroradiologists as the gold standard. The average volume of "abnormal" white matter in normal subjects (false positive) was 0.11 ml (range 0-0.59 ml). In MS patients the average WML volume was 31.0 ml (range 1.1-132.5 ml), with a sensitivity of 87.3%. In the reproducibility study, the mean SD of WML volumes was 2.9 ml. The procedure appears suitable for monitoring disease changes over time. J. Magn. Reson. Imaging 2000;12:799-807.     

Accuracy of metabolic volume and total glycolysis among six threshold-based target segmentation algorithms

Annals of Nuclear Medicine - This study aimed to evaluate the accuracy of six threshold-based segmentation methods with different target-to-background ratios (TBR), images with different voxel...     

Brain volume, intracranial volume, and dementia

RATIONALE AND OBJECTIVES: Using a large magnetic resonance (MR) imaging data set (n = 532), we investigated the utility of total intracranial volume (TICV) as a correction factor for head size variability when assessing total brain volume (TBV) and the subcortical volumes of the temporal horn of the lateral ventricular system and the hippocampus. METHODS: A uniform tissue segmentation procedure (analyze) was used to calculate volumes. Total brain volume was compared with TICV in 357 control subjects and 175 patients with various dementing and neuropsychiatric disorders (mixed dementia/neuropsychiatric group). These MR-based TBV/TICV relationships were compared with actual postmortem (n = 87) values obtained from a study of neurologically healthy subjects at the time of death. Comparisons were also made in which temporal horn and hippocampal volumes were corrected by TICV and TBV. Lastly, the ability of corrected TBV and temporal horn and hippocampal volumes to distinguish subjects in the mixed dementia/neuropsychiatric group from controls was examined by logistic regression. RESULTS: In the control sample, brain volume averaged 9% of TICV, regardless of age. In contrast, TBV in the mixed dementia/neuropsychiatric subjects showed, on average, a 22% reduction compared with TICV. By plotting TBV/TICV curves, highly significant but different regression lines emerged, wherein a reduction in brain volume in conditions of mixed dementia/neuropsychiatric disorder showed a distinct separation from the norm. The TBV/TICV regression line generated from MR imaging in controls did not differ from the postmortem TBV/TICV regression line. Logistic regression showed a 96% correct classification of mixed dementia/neuropsychiatric subjects from controls by using the TBV/TICV ratio. This technique has the advantage that each subject serves as his or her own control. CONCLUSIONS: In cases of dementia and neuropsychiatric disorder in persons 65 and older, TBV corrected by TICV readily differentiated this clinical population from controls. This technique is easy and simple to use and has various clinical applications. For temporal horn and hippocampal volume, corrections with TBV rather than TICV may provide more clinically meaningful corrections.     

Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT

The objective of this study was to develop a computing tool for full-automatic segmentation of body fat distributions on volumetric CT images. We developed an algorithm to automatically identify the body perimeter and the inner contour that separates visceral fat from subcutaneous fat. Diaphragmatic surfaces can be extracted by model-based segmentation to match the bottom surface of the lung in CT images for determination of the upper limitation of the abdomen. The functions for quantitative evaluation of abdominal obesity or obesity-related metabolic syndrome were implemented with a prototype three-dimensional (3D) image processing workstation. The volumetric ratios of visceral fat to total fat and visceral fat to subcutaneous fat for each subject can be calculated. Additionally, color intensity mapping of subcutaneous areas and the visceral fat layer is quite obvious in understanding the risk of abdominal obesity with the 3D surface display. Preliminary results obtained have been useful in medical checkups and have contributed to improved efficiency in checking obesity throughout the whole range of the abdomen with 3D visualization and analysis.     

A new semiautomated,three-dimensional technique allowing precise quantification of total and regional cerebellar volume using MRI

A new method was developed to measure total and regional cerebellar volumes using MRI. Previously, the volumes of the cerebellum and its substructure had been studied planimetri-cally. The new method uses three-dimensional semiautomated volumetry with focus on reliability and performance. The method consists of a manual presegmentation using landmark-adjusted planes followed by region-growing segmentation and calculation of volume. The cerebellum is partitioned into 11 regions defined by planes, which are adjusted for internal cerebellar landmarks (three radial regions inside the vermis that extend into the medial hemisphere (one-fourth of the transverse diameter of the hemisphere); one region in the lateral hemisphere (remaining three-fourths)). Forty-six healthy volunteers were examined and the effects of age, gender, and symmetry were estimated. Shrinkage in the vermis (especially anterior superior compartment) was marked. Age effects diminished laterally and were not observed in the lateral hemisphere. Age effects on the total cerebellar volume were marginal. Effects of gender and symmetry were nonsignificant. Technique and results are discussed and related to methods and findings of others.     

Automated segmentation of MR images of brain tumors

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.     

Automated three-dimensional quantification of myocardial perfusion and brain SPECT.

To allow automated and objective reading of nuclear medicine tomography, we have developed a set of tools for clinical analysis of myocardial perfusion tomography (PERFIT) and Brain SPECT/PET (BRASS). We exploit algorithms for image registration and use three-dimensional (3D) "normal models" for individual patient comparisons to composite datasets on a "voxel-by-voxel basis" in order to automatically determine the statistically significant abnormalities. A multistage, 3D iterative inter-subject registration of patient images to normal templates is applied, including automated masking of the external activity before final fit. In separate projects, the software has been applied to the analysis of myocardial perfusion SPECT, as well as brain SPECT and PET data. Automatic reading was consistent with visual analysis; it can be applied to the whole spectrum of clinical images, and aid physicians in the daily interpretation of tomographic nuclear medicine images.     

Automatic segmentation of the brain and intracranial cerebrospinal fluid in T1-weighted volume MRI scans of the head, and its application to serial cerebral and intracranial volumetry.

A new fully automatic algorithm for the segmentation of the brain and total intracranial cerebrospinal fluid (CSF) from T(1)-weighted volume MRI scans of the head, called Exbrain v.2, is described. The algorithm was developed in the context of serial intracranial volumetry. A brain mask obtained using a previous version of the algorithm forms the basis of the CSF segmentation. Improved brain segmentation is then obtained by iterative tracking of the brain-CSF interface. Gray matter (GM), white matter (WM), and intracranial CSF volumes and probability maps are calculated based on a model of intensity probability distribution (IPD) that includes two partial volume classes: GM-CSF and GM-WM. Accuracy was assessed using the Montreal Neurological Institute's (MNI) digital phantom scan. Reproducibility was assessed using scan pairs from 24 controls and 10 patients with epilepsy. Segmentation overlap with the gold standard was 98% for the brain and 95%, 96%, and 97% for the GM, WM, and total intracranial contents, respectively; CSF overlap was 86%. In the controls, the Bland and Altman coefficient of reliability (CR) was 35.2 cm(3) for the total brain volume (TBV) and 29.0 cm(3) for the intracranial volume (ICV). Scan-matching reduced CR to 25.2 cm(3) and 17.1 cm(3) for the TBV and ICV, respectively. For the patients, similar CR values were obtained for the ICV.     

Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study

Ethics committees approved human and animal study components; informed written consent was provided (prospective human study [20 men; mean age, 62 years]) or waived (retrospective human study [16 men, four women; mean age, 59 years]). The purpose of this study was to prospectively evaluate a clinically applicable method, accounting for the partial volume effect, to automatically quantify myocardial infarction from delayed contrast material-enhanced magnetic resonance images. Pixels were weighted according to signal intensity to calculate infarct fraction for each pixel. Mean bias +/- variability (or standard deviation), expressed as percentage left ventricular myocardium (%LVM), were -0.3 +/- 1.3 (animals), -1.2 +/- 1.7 (phantoms), and 0.3 +/- 2.7 (patients), respectively. Algorithm had lower variability than dichotomous approach (2.7 vs 7.7 %LVM, P < .01) and did not differ from interobserver variability for bias (P = .31) or variability (P = .38). The weighted approach provides automatic quantification of myocardial infarction with higher accuracy and lower variability than a dichotomous algorithm.