Radonc/BME Medical Image and
Computational Analysis Laboratory
Segmenting and characterizing pulmonary vasculature tree structure.



We were initially interested separating the pulmonary vascular structures from the regions of radiation fibrosis in the lung for radiation dose response analysis (see that project page here). Dr. R. Jim White at URMC approached us after Sindhuja TG's MS thesis presentation to apply this approach to automatically quantify changes in pulmonary arterial vasculature in a rat model of chronic pulmonary arterial hypertension (PAH). During Ankit Salgia's MS thesis presentation we were asked to extended this work to analysis of pulmonary vascular development in human neonated with extreme pre-term gestation. We are currently applying non-invasive assessment of pulmonary vascular structure to quantify (1) the response of the lung to radiation exposure in breast cancer patients (funded through a grant from the Florida Department of Health), (2) the development of lung vasculature in children born extremely prematurely (student support [ Aren Saini and Alex Ozycz] via the UF Research Scholars Program), and (3) changes in vascular anatomy adults and in rats lungs with various vascular diseases.

Figure 1
A healthy adult human contrast-enhanced chest CT data set. [A] Image A is a maximum intensity projection (MIP) of the CT dataset of the right hemi-lung after extraction of the lung volume. This is a side view of the chest looking outward from the perspective of the heart with the lung apex at the top. [B] Image B is a depth-enhanced MIP of the extracted vessel tree structure resulting from a region-growing algorithm seeded at the root of one of the large vessels in the lower right hemi-lung. The distinct vascular beds of the 3 right lung lobes are apparent in this view. [C] Image C is a depth-enhanced MIP of the skeletonized tree in the same orientation. [D] Image D is a colorized MIP of the simulated image of the 8 largest vessel trees resulting from the splitting of each connected tree. The corrected tree structure was found to have 1,960 branches on 32 independent trees.


We discovered that threshold-based methods of vessel segmentation lead to underestimation of vessel radii. We then adapted (and patented) a method for automatic tumor sizing to vessel sizing. This approach was recently validated using a realistic 3D physical phantom. The phantom was created by taking the segmented vascular tree from Figure 1C, and creating a mathematical representation of the largest tree wherein each branch centerline is repsentated as a spline-based curve in 3D space and the branch surface as a constant-radius tube structure. Branches were connected using a mathematical model of the bifurcation region. From this mathematical model we generated a 3D surface mesh of the tree which was outputted as a stereolithography file what was send to a 3D printer. We then took the 3D printout of the human lung vascular tree and manually measured 69 branches. This tree was then put into a commercial clinical CT scanner and we processed those images using our software, to compare against the manual measurements. This work was shown by student Anne Gormaley at the national BMES conference in 2017 and was published in the journal Medical Physics.
With the help of medical physicist Dr. Izabella Berrato, we have generated 16 different chest CT reconstructions from the same subject raw CT data. Sam Martocci and Shruti Siva Kumar are processing this data with the goal of developing a calibration model to adjust vessel counts for variation in slice thickness, in-plane resolution and imaging filtering.


Lung vasular radiation response
Under an FDOH funded study, we are Lung vascular development in prematurely-born children
We have begun to apply the vessel characterization method to document the development of lung vascular systems in prematurely-born children. Under an IRB-approved retrospective protocol, Aren Saini and Randi Dias are processing 100+ repeat chest CT scans of 30 subjects: half who were born prematurely and half being age-matched controls, to compute histograms of vessel size distribution over time. The initial pilot study results presented at BMES 2018 demonstrated great variability in vessel number depending upon scanner parameters, information we used to refine the datasets in the new protocol.

Arterial tree pruning in rat models of PAH
As demonstrated in Figure 2, the lab of Dr. James White at the university of Rocheester has develop a rat model of chronic pulmonary arterial hypertension. They use micro-CT to obtain 15 micron-resolution images of diseased and control animals, and animlas given an experimental agent. Siri Raviru has shown in her poster at BMES 2019 that we can reliably qantify changes in the vessel histogram for each cohort. We are continuing to process the latest data from the URMC team.

Figure 2
[A] 3D pulmonary arterial tree segmentation result for a healthy rat hemi-lung. Micro-CT images were obtained at approximately 40 micron isotropic resolution.
[B] 3D pulmonary arterial tree segmentation result for a rat with experimentally-induced PAH and demonstrating marked changes/remodeling of the pulmonary arterial tree. [C] Log-histogram of number of branches versus branch radius for the healthy and PHA rat.
      Traversing and sizing vessel trees from 3D medical images (UF Office of Technology Licensing).

Related Publications
  1. O’Dell W, Gormaley A, Prida D.,
    Validation of the Gatortail Method for Accurate Sizing of Pulmonary Vessels from 3D Medical Images
    in Medical Physics, 2017;44(12). doi:10.1002/mp.12580.
Related Presentions
  1. Ravuri S, Siva Kumar S, White RJ, Haight D, O’Dell WG.
    Modeling and quantifying vascular structures in experimental rat lungs.
    Biomedical Engineering Society Annual Meeting. Philadelphia, PA: Oct. 16, 2019.
  2. Saini A, Siva Kumar S, O’Dell W.
    Measuring lung vessel tree growth during development in pediatric patients.
    Biomedical Engineering Society Annual Meeting. Atlanta GA: Oct. 18, 2018.
  3. Gormaley A, Prida D, O'Dell W
    Validation of Vessel Sizing from 3D Medical Images
    Biomedical Engineering Society (BMES) Annual Meeting, October 11-14, 2017 in Phoenix, AZ
  4. Sorrentino, Z., O'Dell, W.G.
    Pulmonary Vessel Analysis to Predict Pulmonary Fibrosis
    UF Medical Student Research Day, August 10, 2016
  5. O'Dell, W.G., Govindarajan, S.T., Salgia, A., Hegde, S., Prabhakaran, S., Finol, E.A., White, R.J.
    Method for traversing and labeling complex vascular tree structures from 3D medical images: description, validation and application
    SPIE Medical Imaging, San Diego CA, Feb. 2014
  6. O'Dell WG, Prabhakaran S, Hegde, S.
    Quantification of 3D Pulmonary Vascular Morphology in Pediatric Patients with Pulmonary Vascular Disease.
    Oral presentation at BMES Scientific Meeting and Exhibition, Seattle WA, October 2013
  7. Salgia A, Govindarajan ST, Haight D, White RJ, O'Dell WG
    Automatic quantification from CT scans of morphological changes in pulmonary aterial vasculature in pulmonary artery hypertension
    BMES Scientific Meeting and Exhibition, Atlanta GA, October 2012
  8. Govindarajan ST, Chandrasekharan S, O'Dell WG
    Automatic Segmentation of Blood Vessel in the presence of Fibrosis in Volumetric Lung CT Images
    BMES Scientific Meeting and Exhibition, Hartford Conn, October 2011