Aneuploidy is typically assessed by flow cytometry (FCM) and image cytometry (ICM). We used optical projection tomographic microscopy (OPTM) for assessing cellular DNA content using absorption and fluorescence stains. OPTM combines some of the attributes of both FCM and ICM and generates isometric high-resolution three-dimensional (3-D) images of single cells. Although the depth of field of the microscope objective was in the submicron range, it was extended by scanning the objective’s focal plane. The extended depth of field image is similar to a projection in a conventional x-ray computed tomography. These projections were later reconstructed using computed tomography methods to form a 3-D image. We also present an automated method for 3-D nuclear segmentation. Nuclei of chicken, trout, and triploid trout erythrocyte were used to calibrate OPTM. Ratios of integrated optical densities extracted from 50 images of each standard were compared to ratios of DNA indices from FCM. A comparison of mean square errors with thionin, hematoxylin, Feulgen, and SYTOX green was done. Feulgen technique was preferred as it showed highest stoichiometry, least variance, and preserved nuclear morphology in 3-D. The addition of this quantitative biomarker could further strengthen existing classifiers and improve early diagnosis of cancer using 3-D microscopy.
A potential biomarker for early diagnosis of cancer is assessment of high nuclear DNA content. Conventional
hematoxylin staining is neither stoichiometric nor reproducible. Although feulgen stain is stoichiometric, it is time
consuming and destroys nuclear morphology. We used acidic thionin stain, which can be stoichiometric and also
preserve the nuclear morphology used in conventional cytology. Fifty chicken erythrocyte nuclei singlets (CENs), diploid trout erythrocyte nuclei (TENs) and Triploid TENs were stained for 15 and 30 minutes each. After imaging with optical projection tomography microscope (OPTM), 3D reconstructions of the nuclei were processed to calculate
chromatin content. The mean of ratios of individual observations was compared with standard ratios of DNA indices
of the flow cytometry standards. Mean error, standard deviation and 97% confidence interval (CI) was computed for
the ratios of these standards. At 15 and 30 minutes, the ratio of Triploid TEN to TEN was 1.72 and 1.76, TEN to CEN
was 1.27 and 2.01 and Triploid TEN to CEN was 2.11 and 3.39 respectively. Estimates of DNA indices for all 3 types
of nuclei had less mean error at 30 minutes of staining; Triploid TEN to TEN 0.349±0.04, TEN to CEN 0.36±0.04 and Triploid TEN to CEN 0.64 ± 0.07. In conclusion, imaging of cells with thionin staining at 30 minutes and 3D reconstruction provides quantitative assessment of cell chromatin content. The addition of this quantitative feature of aneuploidy is expected to add greater accuracy to a classifier for early diagnosis of cancer based on 3D cytological imaging.
The nuclear cytoplasmic ratio (nc-ratio) is one of the measurements made by cytologists in evaluating the state of a
single cell and is defined to be the ratio of the size of the nucleus to the size of the cytoplasm. This ratio is often realized
in practice by measurements on a single 2D image of a cell image acquired from a conventional microscope, and is
determined by the area of the nucleus measured in the 2D image divided by the area of the cytoplasm seen to be outside
of the nuclear region. It may also be defined as the ratio of the volume of the nucleus to volume of the cytoplasm, but
this is not directly observable in single images from conventional 2-dimensional microscopy.
We conducted a study to evaluate the variation of the 2D nc-ratio estimation due to the asymmetric architecture of cells
and to compare the 2D estimates with the more precise volumetric nc-ratio estimation from 3D cell images. The
measurements were made on 232 3D images of five different cell types. The results indicate that the cell orientation may
cause a large amount of variation in the nc-ratio estimation and that nc-ratios computed directly from 3D images, which
are independent of cell orientation, may offer a much more precise and useful measurement.
Cardiovascular disease is a leading cause of death in developed countries. The concurrent detection of heart
diseases during low-dose whole-lung CT scans (LDCT), typically performed as part of a screening protocol,
hinges on the accurate quantification of coronary calcification. The creation of fully automated methods is
ideal as complete manual evaluation is imprecise, operator dependent, time consuming and thus costly. The
technical challenges posed by LDCT scans in this context are mainly twofold. First, there is a high level
image noise arising from the low radiation dose technique. Additionally, there is a variable amount of cardiac
motion blurring due to the lack of electrocardiographic gating and the fact that heart rates differ between
human subjects. As a consequence, the reliable segmentation of the heart, the first stage toward the
implementation of morphologic heart abnormality detection, is also quite challenging. An automated
computer method based on a sequential labeling of major organs and determination of anatomical landmarks
has been evaluated on a public database of LDCT images. The novel algorithm builds from a robust
segmentation of the bones and airways and embodies a stepwise refinement starting at the top of the lungs
where image noise is at its lowest and where the carina provides a good calibration landmark. The
segmentation is completed at the inferior wall of the heart where extensive image noise is accommodated.
This method is based on the geometry of human anatomy and does not involve training through manual
markings. Using visual inspection by an expert reader as a gold standard, the algorithm achieved successful
heart and major vessel segmentation in 42 of 45 low-dose CT images. In the 3 remaining cases, the cardiac
base was over segmented due to incorrect hemidiaphragm localization.
This work describes a method that can discriminate between a solid pulmonary nodule and a pulmonary vessel
bifurcation point at a given candidate location on a CT scan using the method of standard moments. The
algorithm starts with the estimation of a spherical window around a nodule candidate center that best captures
the local shape properties of the region. Then, given this window, the standard set of moments, invariant to
rotation and scale is computed over the geometric representation of the region. Finally, a feature vector composed
of the moment values is classified as either a nodule or a vessel bifurcation point.
The performance of this technique was evaluated on a dataset containing 276 intraparenchymal nodules and
276 selected vessel bifurcation points. The method resulted in 99% sensitivity and 80% specificity in identifying
nodules, which makes this technique an efficient filter for false positives reduction. Its efficiency was further
evaluated on the dataset of 656 low-dose chest CT scans. Inclusion of this filter into a design of an experimental
detection system resulted in up to a 69% decrease in false positive rate in detection of intraparenchymal nodules
with less than 1% loss in sensitivity.
The primary stage of a pulmonary nodule detection system is typically a candidate generator that efficiently
provides the centroid location and size estimate of candidate nodules. A scale-normalized Laplacian of Gaussian
(LOG) filtering method presented in this paper has been found to provide high sensitivity along with precise
locality and size estimation. This approach involves a computationally efficient algorithm that is designed to
identify all solid nodules in a whole lung anisotropic CT scan.
This nodule candidate generator has been evaluated in conjunction with a set of discriminative features that
target both isolated and attached nodules. The entire detection system was evaluated with respect to a sizeenriched
dataset of 656 whole-lung low-dose CT scans containing 459 solid nodules with diameter greater than 4
mm. Using a soft margin SVM classifier, and setting false positive rate of 10 per scan, we obtained a sensitivity
of 97% for isolated, 93% for attached, and 89% for both nodule types combined. Furthermore, the LOG filter
was shown to have good agreement with the radiologist ground truth for size estimation.
CT scanners often have higher in-plane resolution than axial resolution. As a result, measurements in the axial direction are less reliable than measurements in-plane, and this should be considered when performing nodule growth measurements. We propose a method to measure nodule growth rates by a moment-based algorithm using the central second order moments for the in-plane directions. The interscan repeatability of the new method was compared to a volumetric measurement method on a database of 22 nodules with multiple scans taken in the same session. The interscan variability was defined as the 95% confidence interval of the relative volume change. For the entire database of nodules, the interscan variability of the volumetric growth method was (-52.1%, 30.1%); the moment-based method improved the variability to (-34.2%, 23.3%). For the 11 nodules with scans of the same slice thickness between scans, the variability of the volumetric growth method was (24.0%, 30.1%), compared to (-12.4%, 12.7%) for the moment-based method. The 11 nodules with scans of different slice thickness had a variability for the volumetric method of (-68.4%, 30.2%) and for the moment-based method, (-46.5%, 24.4%). The moment-based method showed improvement in interscan variability for all cases. This study shows promising preliminary results of improved repeatability of the new moment-based method over a volumetric method and suggests that measurements on scans of the same slice thickness are more repeatable than on scans of different slice thickness. The 11 nodules with the same slice thickness are publicly available.
An estimation of the so called Ground Truth (GT), i.e. the actual lesion region, can minimize readers' subjectivity
if multiple readers' markings are combined. Two methods perform this estimate by considering the
spatial location of voxels: Thresholded Probability-Map (TPM) and Simultaneous Truth and Performance Level
Estimation (STAPLE). An analysis of these two methods has already been performed. The purpose of this study,
however, is gaining a new insight into the method outcomes by comparing the estimated regions. A subset of the
publicly available Lung Image Database Consortium archive was used, selecting pulmonary nodules documented
by all four radiologists. The TPM estimator was computed by assigning to each voxel a value equal to average
number of readers that included such voxel in their markings and then applying a threshold of 0.5. Our STAPLE
implementation is loosely based on a version from ITK, to which we added the graph cut post-processing. The
pair-wise similarities between the estimated ground truths were analyzed by computing the respective Jaccard
coefficients. Then, the sign test of the differences between the volumes of TPM and STAPLE was performed.
A total of 35 nodules documented on 26 scans by all four radiologists were available. The spatial agreement
had a one-sided 90% Confidence Interval of [0.92, 1.00]. The sign test of the differences had a p-value less than
0.001. We found that (a) the differences in their volume estimates are statistically significant, (b) the spatial
disagreement between the two estimators is almost completely due to the exclusion of voxels marked by exactly
two readers, (c) STAPLE tends to weight more, in its GT estimate, readers marking broader regions.
The growth rate of pulmonary nodules has been shown to be an indicator of malignancy, and previous work on pulmonary nodule characterization has suggested that the asymmetry of a nodule's shape may be correlated with malignancy. We have also observed that measurements in the axial direction on CT scans are less repeatable than measurements in-plane and this should be considered when making lesion size-change measurements. To address this, we present a method to measure the asymmetry of a pulmonary nodule's growth by the use of second-order central moments that are insensitive to z-direction variation. The difference in the moment ratios on each scan is used as a measure of the asymmetry of growth. To establish what level of difference is significant, the 95% confidence interval of the differences was determined on a zero-change dataset of 22 solid pulmonary nodules with repeat scans in the same session. This method was applied to a set of 47 solid, stable pulmonary nodules and a set of 49 solid, malignant nodules. The confidence interval established from the zero-change dataset was (-0.45, 0.38); nodules with differences outside this confidence interval are considered to have asymmetric growth. Of the 47 stable nodules, 12.8% (6/47) were found to have asymmetric growth compared to 24.5% (12/49) of malignant nodules. These preliminary results suggest that nodules with asymmetric growth can be identified.
Knowledge of the exact shape of a lesion, or ground truth (GT), is necessary for algorithm validation, measurement
metric analysis, accurate size estimation. When multiple readers provide their documentations of a
lesion that can ultimately be described with occupancy regions, estimating the unknown GT is achieved by aptly
merging those occupancy regions into a single outcome. Several methods are already available but, even when
they consider the spatial location of pixels, e.g. thresholded probability-map (TPM) or STAPLE, pixels are assumed
spatially independent (even when STAPLE proposes a hidden-Markov-random-field fix). In this paper we
propose Truth Estimate from Self Distances (TESD): a new voting scheme, for all the voxels inside and outside
the occupancy region, in order to take in account three key characteristics: (a) critical shape conformations, like
holes or spikes, that are defined by the reciprocally surrounding pixels, (b) marking co-locations, meaning the
closeness without intersection of one reader's marking to other readers' ones and c) the three-dimensionality of
lesions as imaged by CT scanners. In TESD each voxel is labeled into four categories according to its signed
distance transform and then the labeled images are combined with a center of gravity method to provide the GT
estimation. This theoretical approach was validated on a subset of the publicly available Lung Image Database
Consortium archive, where a total of 35 nodules documented on 26 scans by all four radiologists were available.
The results obtained are reasonable estimates, with GT obtained close to TPM and STAPLE; at the same time
this method is not limited to the intersections of readers' marked regions.
Accurate nodule volume estimation is necessary in order to estimate the clinically relevant growth rate or change
in size over time. An automated nodule volume-measuring algorithm was applied to a set of pulmonary nodules
that were documented by the Lung Image Database Consortium (LIDC). The LIDC process model specifies that
each scan is assessed by four experienced thoracic radiologists and that boundaries are to be marked around
the visible extent of the nodules for nodules 3 mm and larger. Nodules were selected from the LIDC database
with the following inclusion criteria: (a) they must have a solid component on a minimum of three CT image
slices and (b) they must be marked by all four LIDC radiologists. A total of 113 nodules met the selection
criterion with diameters ranging from 3.59 mm to 32.68 mm (mean 9.37 mm, median 7.67 mm). The centroid
of each marked nodule was used as the seed point for the automated algorithm. 95 nodules (84.1%) were
correctly segmented, but one was considered not meeting the first selection criterion by the automated method;
for the remaining ones, eight (7.1%) were structurally too complex or extensively attached and 10 (8.8%) were
considered not properly segmented after a simple visual inspection by a radiologist. Since the LIDC specifications,
as aforementioned, instruct radiologists to include both solid and sub-solid parts, the automated method core
capability of segmenting solid tissues was augmented to take into account also the nodule sub-solid parts. We
ranked the distances of the automated method estimates and the radiologist-based estimates from the median
of the radiologist-based values. The automated method was in 76.6% of the cases closer to the median than at
least one of the values derived from the manual markings, which is a sign of a very good agreement with the
radiologists' markings.
Differences in the size distribution of malignant and benign pulmonary nodules in databases used for training and testing characterization systems have a significant impact on the measured performance. The magnitude of this effect and methods to provide more relevant performance results are explored in this paper. Two- and three-dimensional features, both including and excluding size, and two classifiers, logistic regression and distance-weighted nearest-neighbors (dwNN), were evaluated on a database of 178 pulmonary nodules. For the full database, the area under the ROC curve (AUC) of the logistic regression classifier for 2D features with and without size was 0.721 and 0.614 respectively, and for 3D features with and without size, 0.773 and 0.737 respectively. In comparison, the performance using a simple size-threshold classifier was 0.675. In the second part of the study, the performance was measured on a subset of 46 nodules from the entire subset selected to have a similar size-distribution of malignant and benign nodules. For this subset, performance of the size-threshold was 0.504. For logistic regression, the performance for 2D, with and without size, were 0.578 and 0.478, and for 3D, with and without size, 0.671 and 0.767. Over all the databases, logistic regression exhibited better performance using 3D features than 2D features. This study suggests that in systems for nodule classification, size is responsible for a large part of the reported performance. To address this, system performance should be reported with respect to the performance of a size-threshold classifier.
Size is an important metric for pulmonary nodule
characterization. Furthermore, it is an important parameter in
measuring the performance of computer aided detection systems since
they are always qualified with respect to a given size range of
nodules. The first 120 whole-lung CT scans documented by the Lung Image
Database Consortium using their protocol for nodule evaluation
were used in this study. For documentation, each inspected lesion was
reviewed independently by four expert radiologists and, when a lesion
was considered to be a nodule larger than 3mm, the radiologist
provided boundary markings in each image in which the nodule was
contained. Three size metrics were considered: a uni-dimensional and
a bi-dimensional measure on a single image slice and a volumetric
measurement based on all the image slices. In this study we analyzed
the boundary markings of these nodules in the context of these three
size metrics to characterize the inter-radiologist variation and to
examine the difference between these metrics. A data set of 63 nodules
each having four observations was analyzed for inter-observer
variation and an extended set of 252 nodules each having at least one
observation was analyzed for the difference between the metrics. A
very high inter-observer variation was observed for all these metrics
and also a very large difference among the metrics was observed.
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