TubeTK/Data

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Data

100 Healthy Normal MRIs and MRAs from UNC

Intracranial-Teaser.jpg This dataset contains 100 (T1, MRA) image pairs from healthy patients, acquired by a Siemens Allegra head-only 3T MR system. The voxel spacing for the MRA images is (0.5x0.5x0.8mm) with a volume size of (448x448x128voxel); The voxel spacing for the T1 images is 1mm isotropic with a volume size of (176x256x176voxel).

The full dataset can be downloaded here. A subset of this dataset also includes intra-cranial vasculature (centerline + radius), extracted from the MRA images. These models are found in each patient's "Auxillary Data" folder, such as here.



Recognition in Ultrasound (REUS) Database

REUS-Teaser.jpg This dataset contains Ultrasound (US) videos, acquired on a (hand-made) noodle phantom. The database is split into template videos and search path videos and is designed to evaluate recognition algorithms. It can be downloaded from here.

In summary (see included README file for details), the database contains videos from 9 different noodle structures. We provide 10 templates videos per structure (i.e., 90 templates in total) and 9 search path videos (i.e., 1 search path video per structure). One task could be, for instance, to localize (temporally) the templates in the corresponding search path videos. Currently, the videos are available in (64x64pixel) and (128x128pixel) spatial resolution.

To evaluate recognition algorithms, we further provide human ground truth locations for the search paths, indicating when a structure moves "in" and "out" of the US imaging plane (These annotations were averaged over 5 different persons).


TubeTK Testing Data

Another good resource to download data material for experimenting with TubeTK is our testing data, available from here.

Patents

  • 6,690,816 : "Systems and methods for tubular object processing" Stephen R. Aylward, Elizabeth Bullitt, Daniel Fritsch, Stephen M. Pizer, February 2004
    • Patent is held by UNC. A world-wide, unrestricted, free license has been granted.

Books

S. Aylward and E. Bullitt, Chapter 11: Clinical Applications Involving Vascular Image Segmentation and Registration. Angiography and Plaque Imaging: Advanced Segmentation Tequniques, Academic Press, 2002 pp. 529-579

Journal Articles

  • R. Kwitt, N. Vasconcelos, S. Razzaque and S.R. Aylard: Localizing Target Structures in Ultrasound Video - A Phantom Study, Medical Image Analysis, vol. 17, no 7., pp. 7127-22, 2013 Publisher Link Preprint
  • R. C. Gessner, S. R. Aylward, and P. A. Dayton: Mapping Microvasculature with Acoustic Angiography Yields Quantifiable Differences between Healthy and Tumor-bearing Tissue Volumes in a Rodent Model, Radiology, vol. 264, no. 3, pp. 733–740, 2012.
  • E. Bullitt, D. Zeng, B. Mortamet, A. Ghosh, S. Aylward, W. Lin, B. L. Marks, and K. Smith: The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography. Neurobiology of Aging, vol. 31, no. 2, pp. 290–300, 2010.
  • E. Bullitt, M. Ewend, J. Vredenburgh, A. Friedman, W. Lin, K. Wilber, D. Zeng, S. Aylward, and D. Reardon: Computerized assessment of vessel morphological changes during treatment of glioblastoma multiforme: Report of a case imaged serially by MRA over four years. NeuroImage, vol. 47, pp. T143–T151, 2009.
  • G. Piliere, M. H. Van Horn, R. Dixon, J. Stavas, S. Aylward, and E. Bullitt: Vessel target location estimation during the TIPS procedure. Medical Image Analysis, vol. 13, no. 3, pp. 519–529, 2009.
  • E. Bullitt, S. R. Aylward, T. Van Dyke, and W. Lin: Computer-assisted measurement of vessel shape from 3T magnetic resonance angiography of mouse brain, Methods, vol. 43, no. 1, pp. 29–34, 2007.
  • S. Aylward, K. Cleary, and D. Hawkes: Intraoperative image processing for surgical guidance. IEEE Transactions on Medical Imaging, vol. 24, no. 11, pp. 1401–1404, 2005.
  • E. Bullitt, D. Zeng, G. Gerig, S. Aylward, S. Joshi, J. Smith, W. Lin, and M. Ewend: Vessel tortuosity and brain tumor malignancy: A blinded study. Academic Radiology, vol. 12, no. 10, pp. 1232–1240, 2005 (Winner 2006 Herbert M. Stauffer Award by the Association of University Radiologists PMID: 16179200)
  • E. Bullitt, K. Muller, I. Jung, W. Lin, and S. Aylward: Analyzing attributes of vessel populations. Medical Image Analysis, vol. 9, no. 1, pp. 39–49, 2005.
  • E. Bullitt, M. Ewend, S. Aylward, W. Lin, G. Gerig, S. Joshi, I. Jung, K. Muller, and J. Smith: Abnormal vessel tortuosity as a marker of treatment response of malignant gliomas: Preliminary report. Technology in Cancer Research & Treatment, vol. 3, no. 6, pp. 577–584, 2004.
  • Y. Fridman, S. Pizer, S. Aylward, and E. Bullitt: Extracting branching tubular object geometry via cores. Medical Image Analysis, vol. 8, no. 3, pp. 169–176, 2004.
  • J. Jomier, S. Weeks, and S. R. Aylward: Vascular image registration for intra-operative 3D ultrasound annotation. International Congress Series, vol. 1268, pp. 1308–1308, 2004.
  • S. Aylward, J. Jomier, S. Weeks, and E. Bullitt: Registration and analysis of vascular images. International Journal of Computer Vision, vol. 55, no. 2–3, pp. 123–138, 2003.
  • E. Bullitt, G. Gerig, S. Pizer, W. Lin, and S. Aylward: Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE Transactions on Medical Imaging, vol. 22, no. 9, pp. 1163–1171, 2003.
  • D. Wallace, A. Capone, J. Jomier, S. Aylward, M. Landers, M. Trese, and P.-R. S. Grp: Computer automated quantification of plus disease in RetCam images of retinopathy of prematurity. Investigative Ophthamology & Visual Science, vol. 44, no. 1, p. U116, 2003.
  • D. Wallace, J. Jomier, S. Aylward, and M. Landers: Computer-automated quantification of plus disease in retinopathy of prematurity. Journal of AAPOS, vol. 7, no. 2, pp. 126–130, 2003.
  • E. Bullitt and S. Aylward: Patient-specific vascular models for endovascular and open operative procedures. International Congress Series, vol. 1247, pp. 129–138, 2002.
  • S. R. Aylward and E. Bullitt: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Transactions on Medical Imaging, vol. 21, no. 2, pp. 61–75, 2002.
  • E. Bullitt, S. Aylward, K. Smith, S. Mukherji, M. Jiroutek, and K. Muller: Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms. Medical Image Analysis, vol. 5, no. 2, pp. 157–169, 2001.
  • E. Bullitt, S. Aylward, E. Bernard, and G. Gerig: Computer-assisted visualization of arteriovenous malformations on the home personal computer. Neurosurgery, vol. 48, no. 3, pp. 576–582, 2001.
  • E. Bullitt, A. Liu, S. Aylward, C. Coffey, J. Stone, S. Mukherji, K. Muller, and S. Pizer: Registration of 3D cerebral vessels with 2D digital angiograms: Clinical evaluation. Academic Radiology, vol. 6, no. 9, pp. 539–546, 1999.
  • S. Aylward, E. Bullitt, and P. Stephen: VTree3D: Fast and effective vessel, bronchial tube, duct, and bone display. Radiology, vol. 209P, no. S, p. 394, 1998.
  • E. Bullitt, A. Liu, S. Aylward, M. Soltys, J. Rosenman, and S. Pizer: Methods for displaying intracerebral vascular anatomy. Americal Journal of Neuroradiology, vol. 18, no. 3, pp. 417–420, Mar. 1997.

IPMI and MICCAI Publications

  • R. Kwitt, D. Pace, M. Niethammer and S. Aylward: Studying Cerebral Vasculature Using Structure Proximity and Graph Kernels. In: MICCAI '13, vol. 8150, pp. 534-541, 2013 Supplementary Material PDF
  • R. Kwitt, N. Vasconcelos, S. Razzaque and S. Aylward: Recognition in Ultrasound Videos: Where am. In: MICCAI '12, vol. 7512, pp. pp 83-90, 2013 (Oral presentation ; Acceptance rate < 5%; Winner of the MICCAI Young Scientist Award) PDF
  • J. Jomier, E. Bullitt, M. Van Horn, C. Pathak, and S. Aylward: 3D/2D model-to-image registration applied to TIPS surgery. In: MICCAI’06, vol. 4191, pp. 662–669, 2006
  • J. Jomier, V. LeDigarcher, and S. Aylward: Automatic vascular tree formation using the mahalanobis distance. In: MICCAI '05, vol. 3750, pp. 806–812, 2005
  • J. Jomier, V. LeDigarcher, and S. Aylward: Comparison of vessel segmentations using STAPLE. In: MICCAI '05, vol. 3749, pp. 523–530, 2005
  • S. Aylward, J. Jomier, C. Vivert, V. LeDigarcher, and E. Bullitt: Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination. In: MICCAI '05, vol. 3749, pp. 59–66, 2005
  • J. Jomier and S. Aylward: Rigid and deformable vasculature-to-image registration: A hierarchical approach. In: MICCAI '04, vol. 3216, pp. 829–836, 2004
  • E. Bullitt, I. Jung, K. Muller, G. Gerig, S. Aylward, S. Joshi, K. Smith, W. Lin, and M. Ewend: Determining malignancy of brain tumors by analysis of vessel shape. In: MICCAI '04, vol. 3217, pp. 645–653, 2004
  • J. Jomier, D. Wallace, and S. Aylward: Quantification of retinopathy of prematurity via vessel segmentation. In: MICCAI '03, vol. 2879, pp. 620–626, 2003
  • B. Jolly, M. Van Horn, S. Aylward, and E. Bullitt: Needle detection and tracking in the TIPS endovascular procedure. In: MICCAI '03, vol. 2879, pp. 953–954, 2003
  • Y. Fridman, S. Pizer, S. Aylward, and E. Bullitt: Segmenting 3D branching tubular structures using cores. In: MICCAI '03, vol. 2879, pp. 570–577, 2003
  • D. Cool, D. Chillet, J. Kim, J. Guyon, M. Foskey, and S. Aylward: Tissue-based affine registration of brain images to form a vascular density atlas. In: MICCAI '03, vol. 2879, pp. 9–15, 2003
  • D. Chillet, J. Jomier, D. Cool, and S. Aylward: Vascular atlas formation using a vessel-to-image affine registration method. In: MICCAI '03, vol. 2878, pp. 335–342, 2003
  • E. Bullitt, G. Gerig, S. Aylward, S. Joshi, K. Smith, M. Ewend, and W. Lin: Vascular attributes and malignant brain tumors. In: MICCAI '03, vol. 2878, pp. 671–679, 2003
  • S. Aylward, S. Weeks, and E. Bullitt: Analysis of the Parameter Space of a Metric for Registering 3D Vascular Images. In: MICCAI ’01, vol. pp. 932–939, 2001
  • E. Bullitt, S. Aylward, A. Liu, J. Stone, S. Mukherji, C. Coffey, G. Gerig, and S. Pizer: 3D graph description of the intracerebral vasculature from segmented MRA and tests of accuracy by comparison with X-ray angiograms. In: IPMI '99, vol. 1613, pp. 308–321, 1999
  • E. Bullitt, A. Liu, S. Aylward, and S. Pizer: Reconstruction of the intracerebral vasculature from MRA and a pair of projection views. In: IMPI '97, vol. 1230, pp. 537–542, 1997

Other Conference Papers

  • D. F. Pace, A. Enquobahrie, H. Yang, S. R. Aylward, and M. Niethammer: Deformable image registration of sliding organs using anisotropic diffusive regularization. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 407–413, 2011
  • D. F. Pace, M. Niethammer, and S. R. Aylward: Sliding geometries in deformable image registration. In: MICCAI ’11 Proceedings of the Third International Conference on Abdominal Imaging, pp. 141–148, 2011
  • R. C. Gessner, R. Kothadia, S. Feingold, S. Aylward, E. Bullitt, and P. A. Dayton: Blood vessel structural morphology derived from 3D dual-frequency ultrasound images. In: IEEE Ultrasonics Symposium (IUS), pp. 209–212, 2010
  • J. Lee, J. Jomier, S. Aylward, M. Tyszka, S. Moy, J. Lauder, and M. Styner: Evaluation of atlas based mouse brain segmentation. In: SPIE Medical Imaging, 2009
  • Z. Zhao, D. K. Wallace, S. F. Freedman, and S. R. Aylward: A tool for computer-aided diagnosis of retinopathy of prematurity. In: SPIE Medical Imaging, 2008
  • E. Bullitt and S. Aylward: Visualizing blood vessel trees in three dimensions: clinical applications. In: SPIE Medical Imaging, 2005
  • S. R. Aylward, J. Jomier, J. P. Guyon, and S. Weeks: Intra-operative 3D ultrasound augmentation. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 421–424, 2002
  • E. Bullitt, SR Aylward: Patient-specific vascular models for endovascular and open operative procedures. In: Proceedings of the 2nd International Mt. BANDAI Symposium for Neuroscience, pp. 88-94, 2001
  • E. Bullitt and S. R. Aylward: Analysis of Time-Varing Images Using 3-D Vascular Models. In: Proceedings of the 30th on Applied Imagery Pattern Recognition Workshop (AIPR), p. 9-14, 2001 (Keynote Address)
  • S. Aylward, S. Pizer, D. Eberly, and E. Bullitt: Intensity Ridge and Widths for Tubular Object Segmentation and Description. In: MMBIA ’96 p. 131-138, 1996