Yiting Xie

Automated pulmonary nodule CT image characterization in lung cancer screening.

PurposeIn lung cancer screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed.MethodsA computer system that incorporates novel 3D image features to determine the malignancy status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and ELCAP lung cancer studies. The system is evaluated with different data subsets to determine the impact of class size distribution imbalance in datasets and to evaluate different training and testing strategies.ResultsResults show a modest improvement in malignancy prediction compared to prediction by size alone for a traditional size-unbalanced dataset. Further, the advantage of size binning for classifier design and the advantages of a size-balanced dataset for both training and testing are demonstrated.ConclusionNodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.

Automated coronary artery calcification detection on low-dose chest CT images

Coronary artery calcification (CAC) measurement from low-dose CT images can be used to assess the risk of coronary artery disease. A fully automatic algorithm to detect and measure CAC from low-dose non-contrast, non-ECG-gated chest CT scans is presented. Based on the automatically detected CAC, the Agatston score (AS), mass score and volume score were computed. These were compared with scores obtained manually from standard-dose ECG-gated scans and low-dose un-gated scans of the same patient. The automatic algorithm segments the heart region based on other pre-segmented organs to provide a coronary region mask. The mitral valve and aortic valve calcification is identified and excluded. All remaining voxels greater than 180HU within the mask region are considered as CAC candidates. The heart segmentation algorithm was evaluated on 400 non-contrast cases with both low-dose and regular dose CT scans. By visual inspection, 371 (92.8%) of the segmentations were acceptable. The automated CAC detection algorithm was evaluated on 41 low-dose non-contrast CT scans. Manual markings were performed on both low-dose and standard-dose scans for these cases. Using linear regression, the correlation of the automatic AS with the standard-dose manual scores was 0.86; with the low-dose manual scores the correlation was 0.91. Standard risk categories were also computed. The automated method risk category agreed with manual markings of gated scans for 24 cases while 15 cases were 1 category off. For low-dose scans, the automatic method agreed with 33 cases while 7 cases were 1 category off.

Segmentation of the sternum from low-dose chest CT images

Segmentation of the sternum in medical images is of clinical significance as it frequently serves as a stable reference to image registration and segmentation of other organs in the chest region. In this paper we present a fully automated algorithm to segment the sternum in low-dose chest CT images (LDCT). The proposed algorithm first locates an axial seed slice and then segments the sternum cross section on the seed slice by matching a rectangle model. Furthermore, it tracks and segments the complete sternum in the cranial and caudal direction respectively through sequential axial slices starting from the seed slice. The cross section on each axial slice is segmented using score functions that are designed to have local maxima at the boundaries of the sternum. Finally, the sternal angle is localized. The algorithm is designed to be specifically robust with respect to cartilage calcifications and to accommodate the high noise levels encountered with LDCT images. Segmentation of 351 cases from public datasets was evaluated visually with only 1 failing to produce a usable segmentation. 87.2% of the 351 images have good segmentation and 12.5% have acceptable segmentation. The sternal body segmentation and the localization of the sternal angle and the vertical extents of the sternum were also evaluated quantitatively for 25 good cases and 25 acceptable cases. The overall weighted mean DC of 0.897 and weighted mean distance error of 2.88 mm demonstrate that the algorithm achieves encouraging performance in both segmenting the sternal body and localizing the sternal angle.

Automated measurement of pulmonary artery in low-dose non-contrast chest CT images

A new measurement of the pulmonary artery diameter is obtained where the artery may be robustly segmented between the heart and the artery bifurcation. An automated algorithm is presented that can make this pulmonary artery measurement in low-dose non-contrast chest CT images. The algorithm uses a cylinder matching method following geometric constraints obtained from other adjacent organs that have been previously segmented. This new measurement and the related ratio of pulmonary artery to aortic artery measurement are compared to traditional manual approaches for pulmonary artery characterization. The algorithm was qualitatively evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets; 324 out of the 347 cases had good segmentations and in the other 23 cases there was significant boundary inaccuracy. For quantitative evaluation, the comparison was to manually marked pulmonary artery boundary in an axial slice in 45 cases; the resulting average Dice Similarity Coefficient was 0.88 (max 0.95, min 0.74). For the 45 cases with manual markings, the correlation between the automated pulmonary artery to ascending aorta diameter ratio and manual ratio at pulmonary artery bifurcation level was 0.81. Using Bland-Altman analysis, the mean difference of the two ratios was 0.03 and the limits of agreement was (-0.12, 0.18). This automated measurement may have utility as an alternative to the conventional manual measurement of pulmonary artery diameter at the bifurcation level especially in the context of noisy low-dose CT images.

Image segmentation evaluation for very-large datasets

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images

Cardiac visceral fat was segmented from low-dose non-contrast chest CT images using a fully automated method. Cardiac visceral fat is defined as the fatty tissues surrounding the heart region, enclosed by the lungs and posterior to the sternum. It is measured by constraining the heart region with an Anatomy Label Map that contains robust segmentations of the lungs and other major organs and estimating the fatty tissue within this region. The algorithm was evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets. Based on visual inspection, 343 cases had good cardiac visceral fat segmentation. For quantitative evaluation, manual markings of cardiac visceral fat regions were made in 3 image slices for 45 low-dose scans and the Dice similarity coefficient (DSC) was computed. The automated algorithm achieved an average DSC of 0.93. Cardiac visceral fat volume (CVFV), heart region volume (HRV) and their ratio were computed for each case. The correlation between cardiac visceral fat measurement and coronary artery and aortic calcification was also evaluated. Results indicated the automated algorithm for measuring cardiac visceral fat volume may be an alternative method to the traditional manual assessment of thoracic region fat content in the assessment of cardiovascular disease risk.

Single 3D cell segmentation from optical CT microscope images

The automated segmentation of the nucleus and cytoplasm regions in 3D optical CT microscope images has been achieved with two methods, a global threshold gradient based approach and a graph-cut approach. For the first method, the first two peaks of a gradient figure of merit curve are selected as the thresholds for cytoplasm and nucleus segmentation. The second method applies a graph-cut segmentation twice: the first identifies the nucleus region and the second identifies the cytoplasm region. Image segmentation of single cells is important for automated disease diagnostic systems. The segmentation methods were evaluated with 200 3D images consisting of 40 samples of 5 different cell types. The cell types consisted of columnar, macrophage, metaplastic and squamous human cells and cultured A549 cancer cells. The segmented cells were compared with both 2D and 3D reference images and the quality of segmentation was determined by the Dice Similarity Coefficient (DSC). In general, the graph-cut method had a superior performance to the gradient-based method. The graph-cut method achieved an average DSC of 86% and 72% for nucleus and cytoplasm segmentations respectively for the 2D reference images and 83% and 75% for the 3D reference images. The gradient method achieved an average DSC of 72% and 51% for nucleus and cytoplasm segmentation for the 2D reference images and 71% and 51% for the 3D reference images. The DSC of cytoplasm segmentation was significantly lower than for the nucleus since the cytoplasm was not differentiated as well by image intensity from the background.

Individual bone structure segmentation and labeling from low-dose chest CT

The segmentation and labeling of the individual bones serve as the first step to the fully automated measurement of skeletal characteristics and the detection of abnormalities such as skeletal deformities, osteoporosis, and vertebral fractures. Moreover, the identified landmarks on the segmented bone structures can potentially provide relatively reliable location reference to other non-rigid human organs, such as breast, heart and lung, thereby facilitating the corresponding image analysis and registration. A fully automated anatomy-directed framework for the segmentation and labeling of the individual bone structures from low-dose chest CT is presented in this paper. The proposed system consists of four main stages: First, both clavicles are segmented and labeled by fitting a piecewise cylindrical envelope. Second, the sternum is segmented under the spatial constraints provided by the segmented clavicles. Third, all ribs are segmented and labeled based on 3D region growing within the volume of interest defined with reference to the spinal canal centerline and lungs. Fourth, the individual thoracic vertebrae are segmented and labeled by image intensity based analysis in the spatial region constrained by the previously segmented bone structures. The system performance was validated with 1270 lowdose chest CT scans through visual evaluation. Satisfactory performance was obtained respectively in 97.1% cases for the clavicle segmentation and labeling, in 97.3% cases for the sternum segmentation, in 97.2% cases for the rib segmentation, in 94.2% cases for the rib labeling, in 92.4% cases for vertebra segmentation and in 89.9% cases for the vertebra labeling.

Fully automated breast density assessment from low-dose chest CT

Breast cancer is the most common cancer diagnosed among US women and the second leading cause of cancer death 1 . Breast density is an independent risk factor for breast cancer and more than 25 states mandate its reporting to patients as part of the lay mammogram report 2 . Recent publications have demonstrated that breast density measured from low-dose chest CT (LDCT) correlates well with that measured from mammograms and MRIs 3-4 , thereby providing valuable information for many women who have undergone LDCT but not recent mammograms. A fully automated framework for breast density assessment from LDCT is presented in this paper. The whole breast region is first segmented using an anatomy-orientated novel approach based on the propagation of muscle fronts for separating the fibroglandular tissue from the underlying muscles. The fibroglandular tissue regions are then identified from the segmented whole breast and the percentage density is calculated based on the volume ratio of the fibroglandular tissue to the local whole breast region. The breast region segmentation framework was validated with 1270 LDCT scans, with 96.1% satisfactory outcomes based on visual inspection. The density assessment was evaluated by comparing with BI-RADS density grades established by an experienced radiologist in 100 randomly selected LDCT scans of female subjects. The continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman’s rank correlation 0.91 (p-value < 0.001). After converting the continuous density to categorical grades, the automated density assessment was congruous with the radiologist’s reading in 91% cases.

Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation

Abstract. With the advent of fully automated image analysis and modern machine learning methods, there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. This paper presents a method and implementation for facilitating such datasets that addresses the critical issue of size scaling for algorithm validation and evaluation; current evaluation methods that are usually used in academic studies do not scale to large datasets. This method includes protocols for the documentation of many regions in very large image datasets; the documentation may be incrementally updated by new image data and by improved algorithm outcomes. This method has been used for 5 years in the context of chest health biomarkers from low-dose chest CT images that are now being used with increasing frequency in lung cancer screening practice. The lung scans are segmented into over 100 different anatomical regions, and the method has been applied to a dataset of over 20,000 chest CT images. Using this framework, the computer algorithms have been developed to achieve over 90% acceptable image segmentation on the complete dataset.