Zhiyun Gao

Assessment of morphometry of pulmonary acini in mouse lungs by nondestructive imaging using multiscale microcomputed tomography.

Establishing the 3D architecture and morphometry of the intact pulmonary acinus is an essential step toward a more complete understanding of the relationship of lung structure and function. We combined a special fixation method with a unique volumetric nondestructive imaging technique and image processing tools to separate individual acini in the mouse lung. Interior scans of the parenchyma at a resolution of 2 µm enabled the reconstruction and quantitative study of whole acini by image analysis and stereologic methods, yielding data characterizing the 3D morphometry of the pulmonary acinus. The 3D reconstructions compared well with the architecture of silicon rubber casts of mouse acini. The image-based segmentation of individual acini allowed the computation of acinar volume and surface area, as well as estimation of the number of alveoli per acinus using stereologic methods. The acinar morphometry of male C57BL/6 mice age 12 wk and 91 wk was compared. Significant increases in all parameters as a function of age suggest a continuous change of the lung morphometry, with an increase in alveoli beyond what has been previously viewed as the maturation phase of the animals. Our image analysis methods open up opportunities for defining and quantitatively assessing the acinar structure in healthy and diseased lungs. The methods applied here to mice can be adjusted for the study of similarly prepared human lungs.

Topomorphologic Separation of Fused Isointensity Objects via Multiscale Opening: Separating Arteries and Veins in 3-D Pulmonary CT

A novel multiscale topomorphologic approach for opening of two isointensity objects fused at different locations and scales is presented and applied to separating arterial and venous trees in 3-D pulmonary multidetector X-ray computed tomography (CT) images. Initialized with seeds, the two isointensity objects (arteries and veins) grow iteratively while maintaining their spatial exclusiveness and eventually form two mutually disjoint objects at convergence. The method is intended to solve the following two fundamental challenges: how to find local size of morphological operators and how to trace continuity of locally separated regions. These challenges are met by combining fuzzy distance transform (FDT), a morphologic feature with a topologic fuzzy connectivity, and a new morphological reconstruction step to iteratively open finer and finer details starting at large scales and progressing toward smaller scales. The method employs efficient user intervention at locations where local morphological separability assumption does not hold due to imaging ambiguities or any other reason. The approach has been validated on mathematically generated tubular objects and applied to clinical pulmonary noncontrast CT data for separating arteries and veins. The tradeoff between accuracy and the required user intervention for the method has been quantitatively examined by comparing with manual outlining. The experimental study, based on a blind seed selection strategy, has demonstrated that above 95% accuracy may be achieved using 25-40 seeds for each of arteries and veins. Our method is very promising for semiautomated separation of arteries and veins in pulmonary CT images even when there is no object-specific intensity variation at conjoining locations.

A New Paradigm of Interactive Artery/Vein Separation in Noncontrast Pulmonary CT Imaging Using Multiscale Topomorphologic Opening

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for the purpose of diagnosing several pulmonary diseases and to develop new image-based phenotypes. A multiscale topomorphologic opening (MSTMO) algorithm has recently been developed in our laboratory for separating A/V trees via noncontrast pulmonary human CT imaging. The method starts with two sets of seeds-one for each of A/V trees and combines fuzzy distance transform and fuzzy connectivity in conjunction with several morphological operations leading to locally adaptive iterative multiscale opening of two mutually conjoined structures. In this paper, we introduce the methods for handling “local update” and “separators” into our previous theoretical formulation and incorporate the algorithm into an effective graphical user interface (GUI). Results of a comprehensive evaluative study assessing both accuracy and reproducibility of the method under the new setup are presented and also, the effectiveness of the GUI-based system toward improving A/V separation results is examined. Accuracy of the method has been evaluated using mathematical phantoms, CT images of contrast-separated pulmonary A/V casting of a pigs lung and noncontrast pulmonary human CT imaging. The method has achieved 99% true A/V labeling in the cast phantom and, almost, 92-94% true labeling in human lung data. Reproducibility of the method has been evaluated using multiuser A/V separation in human CT data along with contrast-enhanced CT images of a pigs lung at different positive end-expiratory pressures (PEEPs). The method has achieved, almost, 92-98% agreements in multiuser A/V labeling with ICC for A/V measures being over 0.96-0.99. Effectiveness of the GUI-based method has been evaluated on human data in terms of improvements of accuracy of A/V separation results and results have shown 8-22% improvements in true A/V labeling. Both qualitative and quantitative results found are very promising.

Tensor scale-based anisotropic region growing for segmentation of elongated biological structures

Over decades, segmentation has remained a salient task in most medical imaging applications confronting multi-faced challenges including limited image quality. In this paper, we present a new anisotropic region growing segmentation approach for vascular or other elongated structures. A fundamental challenge during tracing vascular structures is broken continuity of structures by noise and other imaging artifacts coupled with leaking through blurring and soft boundaries. Anisotropic region growing solves this problem using tensor scale that captures local structure orientation and geometry using an ellipsoidal model. A new fuzzy connectivity based algorithm is developed that uses tensor scale to facilitate region growing along the local structure while arresting cross-structure leaking. The performance of the method has been quantitatively evaluated on non-contrast human pulmonary CT imaging and the results found are promising.

Multilevel tree analysis of pulmonary artery/vein trees in noncontrast CT images

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

Multi-scale topo-morphometric opening of arteries and veins: an evaluative study via pulmonary CT imaging

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is essential for quantification of vascular geometry useful to diagnose several pulmonary diseases. A multi-scale topomorphologic opening algorithm has recently been introduced separating A/V trees via non-contrast CT imaging. The method starts with two sets of seeds -- one for each of A/V trees and combines fuzzy distance transform, fuzzy connectivity, and morphologic reconstruction leading to locally-adaptive multi-scale opening of two mutually fused structures. Here, we present results of a comprehensive validation study assessing both reproducibility and accuracy of the method. Accuracy of the method is examined using both mathematical phantoms and CT images of contrast-separated pulmonary A/V casting of a pigs lung. Reproducibility of the method is evaluated using multi-user A/V separations of patientss CT pulmonary data and contrast-enhanced CT data of a pigs lung at different volumes. The qualitative and quantitative results are very promising.

A novel multiscale topo-morphometric approach for separating arteries and veins via pulmonary CT imaging

Distinguishing arterial and venous trees in pulmonary multiple-detector X-ray computed tomography (MDCT) images (contrast-enhanced or unenhanced) is a critical first step in the quantification of vascular geometry for purposes of determining, for instance, pulmonary hypertension, using vascular dimensions as a comparator for assessment of airway size, detection of pulmonary emboli and more. Here, a novel method is reported for separating arteries and veins in MDCT pulmonary images. Arteries and veins are modeled as two iso-intensity objects closely entwined with each other at different locations at various scales. The method starts with two sets of seeds -- one for arteries and another for veins. Initialized with seeds, arteries and veins grow iteratively while maintaining their spatial separation and eventually forming two disjoint objects at convergence. The method combines fuzzy distance transform, a morphologic feature, with a topologic connectivity property to iteratively separate finer and finer details starting at a large scale and progressing towards smaller scales. The method has been validated in mathematically generated tubular objects with different levels of fuzziness, scale and noise. Also, it has been successfully applied to clinical CT pulmonary data. The accuracy of the method has been quantitatively evaluated by comparing its results with manual outlining. For arteries, the method has yielded correctness of 81.7% at the cost of 6.7% false positives and 11.6% false negatives. Our method is very promising for automated separation of arteries and veins in MDCT pulmonary images even when there is no mark of intensity variation at conjoining locations.

Multiscale topo-morphologic opening of arteries and veins: a validation study on phantoms and CT imaging of pulmonary vessel casting of pigs

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for purposes of determining, for instance, pulmonary hypertension, detection of pulmonary emboli and more. A multi-scale topo-morphologic opening algorithm has recently been introduced by us separating A/V trees in pulmonary multiple-detector X-ray computed tomography (MDCT) images without contrast. The method starts with two sets of seeds - one for each of A/V trees and combines fuzzy distance transform, fuzzy connectivity, and morphologic reconstruction leading to multi-scale opening of two mutually fused structures while preserving their continuity. The method locally determines the optimum morphological scale separating the two structures. Here, a validation study is reported examining accuracy of the method using mathematically generated phantoms with different levels of fuzziness, overlap, scale, resolution, noise, and geometric coupling and MDCT images of pulmonary vessel casting of pigs. After exsanguinating the animal, a vessel cast was generated using rapid-hardening methyl methacrylate compound with additional contrast by 10cc of Ethiodol in the arterial side which was scanned in a MDCT scanner at 0.5mm slice thickness and 0.47mm in plane resolution. True segmentations of A/V trees were computed from these images by thresholding. Subsequently, effects of distinguishing A/V contrasts were eliminated and resulting images were used for A/V separation by our method. Experimental results show that 92% - 98% accuracy is achieved using only one seed for each object in phantoms while 94.4% accuracy is achieved in MDCT cast images using ten seeds for each of A/V trees.