As part of a more general effort to probe the interrelated factors impacting the accuracy
and precision of lung nodule size estimation, we have been conducting phantom CT
studies with an anthropomorphic thoracic phantom containing a vasculature insert on
which synthetic nodules were inserted or attached. The utilization of synthetic nodules
with known truth regarding size and location allows for bias and variance analysis,
enabled by the acquisition of repeat CT scans. Using a factorial approach to probe
imaging parameters (acquisition and reconstruction) and nodule characteristics (size,
density, shape, location), ten repeat scans have been collected for each protocol and
nodule layout. The resulting database of CT scans is incrementally becoming available to
the public via the National Biomedical Imaging Archive to facilitate the assessment of
lung nodule size estimation methodologies and the development of image analysis
software among other possible applications. This manuscript describes the phantom CT
scan database and associated information including image acquisition and reconstruction
protocols, nodule layouts and nodule truth.
With the advent of high-resolution CT, three-dimensional (3D) methods for nodule volumetry have been introduced,
with the hope that such methods will be more accurate and consistent than currently used planar measures of size.
However, the error associated with volume estimation methods still needs to be quantified. Volume estimation error is
multi-faceted in the sense that there is variability associated with the patient, the software tool and the CT system. A
primary goal of our current research efforts is to quantify the various sources of measurement error and, when possible,
minimize their effects. In order to assess the bias of an estimate, the actual value, or "truth," must be known. In this
work we investigate the reliability of micro CT to determine the "true" volume of synthetic nodules. The advantage of
micro CT over other truthing methods is that it can provide both absolute volume and shape information in a single
measurement. In the current study we compare micro CT volume truth to weight-density truth for spherical, elliptical,
spiculated and lobulated nodules with diameters from 5 to 40 mm, and densities of -630 and +100 HU. The percent
differences between micro CT and weight-density volume for -630 HU nodules range from [-21.7%, -0.6%] (mean=
-11.9%) and the differences for +100 HU nodules range from [-0.9%, 3.0%] (mean=1.7%).
Volumetric CT has the potential to improve the quantitative analysis of lung nodule size
change compared to currently used one-dimensional measurement practices. Towards that
goal, we have been conducting studies using an anthropomorphic phantom to quantify
sources of volume measurement error. One source of error is the measurement technique or
software tool used to estimate lesion volume. In this manuscript, we present a template-based
approach which utilizes the properties of the acquisition and reconstruction system to
quantify nodule volume. This approach may reduce the error associated with the volume
estimation technique, thereby improving our ability to estimate the error directly associated
with CT parameters and nodule characteristics. Our estimation approach consists of: (a) the
simulation of the object-to-image transformation of a helical CT system, (b) the creation of a
bank of simulated 3D nodule templates of varying sizes, and (c) the 3D matching of synthetic
nodules - that were attached to lung vasculature and scanned with a 16-slice MDCT system - to the bank of simulated templates to estimate nodule volume. Results based on 10 repeat
scans for different protocols and a root mean square error (RMSE) similarity metric showed a
relative bias of 88%, 14%, and 4% for the measurement of 5 mm, 8 mm and 10 mm low
density nodules (-630 HU) compared to -3%, -6%, and 8% for nodules of +100HU density.
However, the relative bias for the small, low density nodules (5 mm, -630 HU), was
significantly reduced to 7% when a penalized RMSE metric was used to enforce a symmetry
constraint that reduced the impact of attached vessels. The results are promising for the use
of this measurement approach as a low-bias estimator of nodule volume which will allow the
systematic quantification and ranking of measurement error in volumetric CT analysis of lung
nodules.
High-resolution CT, three-dimensional (3D) methods for nodule volumetry have been introduced, with the hope
that such methods will be more accurate and consistent than currently used planar measures of size. However,
the error associated with volume estimation methods still needs to be quantified. Volume estimation error is
multi-faceted in the sense that it is impacted by characteristics of the patient, the software tool and the CT
system. The overall goal of this research is to quantify the various sources of measurement error and, when
possible, minimize their effects. In the current study, we estimated nodule volume from ten repeat scans of an
anthropomorphic phantom containing two synthetic spherical lung nodules (diameters: 5 and 10 mm; density:
-630 HU), using a 16-slice Philips CT with 20, 50, 100 and 200 mAs exposures and 0.8 and 3.0 mm slice
thicknesses. True volume was estimated from an average of diameter measurements, made using digital calipers.
We report variance and bias results for volume measurements as a function of slice thickness, nodule diameter,
and X-ray exposure.
It is conceivable that a comprehensive clinical case library with intelligent agents can sort and render clinically similar cases and present clinically significant features to assist the radiologist in interpreting mammograms. In this study, we used a deformable vector diagram as the primary framework for matching the mammographic masses. The vector diagram provides gradient and shape features of the mass. The deformable algorithm allows flexible matching. The vector diagram was also incorporated with our newly developed delineation method using steepest changes of a probability based cost-function. Thus it allows us to automatically extract the main body and significant part of border region for pattern matching using a weighted mutual information technique. We have collected 86 mammograms. Of these cases, 46 contain a benign mass and the other 40 contain a malignant mass. Using the weighted mutual information technique on the vector diagram of the mass region, we found that the benign masses can be sorted into 6 groups except one case; the malignant masses can be sorted into 8 groups except two cases. For all 86 cases, the masses can be sorted into 13 groups except three cases. In addition, one group of benign masses and one group of malignant mass cases merged into one which contains 10 cases. Hence, the success sorting rate was 85.7% (12/14) in terms of group and was 84.9% (73/86) in terms of case, respectively.
In this study, a segmentation algorithm based on the steepest changes of a probabilistic cost function was tested on non-processed and pre-processed dense breast images in an attempt to determine the efficacy of pre-processing for dense breast masses. Also, the inter-observer variability between expert radiologists was studied. Background trend correction was used as the pre-processing method. The algorithm, based on searching the steepest changes on a probabilistic cost function, was tested on 107 cancerous masses and 98 benign masses with density ratings of 3 or 4 according to the American College of Radiology's density rating scale. The computer-segmented results were validated using the following statistics: overlap, accuracy, sensitivity, specificity, Dice similarity index, and kappa. The mean accuracy statistic value ranged from 0.71 to 0.84 for cancer cases and 0.81 to 0.86 for benign cases. For nearly all statistics there were statistically significant differences between the expert radiologists.
The purpose of this study is to investigate the efficacy of image features versus likelihood features of tumor boundaries for differentiating benign and malignant tumors and to compare the effectiveness of two neural networks in the classification study: (1) circular processing-based neural network and (2) conventional Multilayer Perceptron (MLP). The segmentation method used is an adaptive region growing technique coupled with a fuzzy shadow approach and maximum likelihood analyzer. Intensity, shape, texture, and likelihood features were calculated for the extracted Region of Interest (ROI). We performed these studies: experiment number 1 utilized image features used as inputs and the MLP for classification, experiment number 2 utilized image features used as inputs and the neural net with circular processing for classification, and experiment number 3 used likelihood values as inputs and the MLP for classification. The experiments were validated using an ROC methodology. We have tested these methods on 51 mammograms using a leave-one-case-out experiment (i.e., Jackknife procedure). The Az values for the four experiments were as follows: 0.66 in experiment number 1, 0.71 in experiment number 2, and 0.84 in experiment number 3.
This study attempted to accurately segment the masses and distinguish malignant from benign tumors. The masses were segmented using a technique that combines pixel aggregation with maximum likelihood analysis. We found that the segmentation method can delineate the tumor body as well as tumor peripheral regions covering typical mass boundaries and some spiculation patterns. We have developed a Multiple Circular Path Convolution Neural Network (MCPCNN) to analyze a set of mass intensity, shape, and texture features for determination of the tumors as malignant or benign. The features were also fed into a conventional neural network for comparison. We also used values obtained from the maximum likelihood values as inputs into a conventional backpropagation neural network. We have tested these methods on 51 mammograms using a grouped Jackknife experiment incorporated with the ROC method. Tumor sizes ranged from 6mm to 3cm. The conventional neural network whose inputs were image features achieved an Az of 0.66. However the MCPCNN achieved an Az value of 0.71. The conventional neural network whose inputs were maximum likelihood values achieved an Az value of 0.84. In addition, the maximum likelihood segmentation method can identify the mass body and boundary regions, which is essential to the analysis of mammographic masses.
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