Three-dimensional image analytical detection of intussusceptive pillars in murine lung.
A variety of diseases can lead to loss of lung tissue. Currently, this can be treated only symptomatically. In mice, a complete compensatory lung growth within 21 days after resection of the left lung can be observed. Understanding and transferring this concept of compensatory lung growth to humans would greatly improve therapeutic options. Lung growth is always accompanied by a process called angiogenesis forming new capillary blood vessels from preexisting ones. Among the processes during lung growth, the formation of transluminal tissue pillars within the capillary vessels (intussusceptive pillars) is observed. Therefore, pillars can be understood as an indicator for active angiogenesis and microvascular remodelling. Thus, their detection is very valuable when aiming at characterization of compensatory lung growth. In a vascular corrosion cast, these pillars appear as small holes that pierce the vessels. So far, pillars were detected visually only based on 2D images. Our approach relies on high-resolution synchrotron microcomputed tomographic images. With a voxel size of 370 nm we exploit the spatial information provided by this imaging technique and present the first algorithm to semiautomatically detect intussusceptive pillars. An at least semiautomatic detection is essential in lung research, as manual pillar detection is not feasible due to the complexity and size of the 3D structure. Using our algorithm, several thousands of pillars can be detected and subsequently analysed, e.g. regarding their spatial arrangement, size and shape with an acceptable amount of human interaction. In this paper, we apply our novel pillar detection algorithm to compute pillar densities of different specimens. These are prepared such that they show different growing states. Comparing the corresponding pillar densities allows to investigate lung growth over time.
Image Processing Department, Fraunhofer ITWM, Kaiserslautern, Germany.|Mathematics Department, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
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S
Föhst
Institute of Functional and Clinical Anatomy, Johannes Gutenberg-Universität Mainz, Mainz, Germany.
W
W
Wagner
Institute of Functional and Clinical Anatomy, Johannes Gutenberg-Universität Mainz, Mainz, Germany.
M
M
Ackermann
Mathematics Department, Technische Universität Kaiserslautern, Kaiserslautern, Germany.
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C
Redenbach
Image Processing Department, Fraunhofer ITWM, Kaiserslautern, Germany.
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K
Schladitz
Image Processing Department, Fraunhofer ITWM, Kaiserslautern, Germany.
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O
Wirjadi
Laboratory of Adaptive and Regenerative Biology, Brigham & Women's Hospital, Harvard Medical School, Boston, USA.
A B
AB
Ysasi
Laboratory of Adaptive and Regenerative Biology, Brigham & Women's Hospital, Harvard Medical School, Boston, USA.
S J
SJ
Mentzer
Institute of Functional and Clinical Anatomy, Johannes Gutenberg-Universität Mainz, Mainz, Germany.
M A
MA
Konerding
D000465:Algorithms; D000818:Animals; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000033:anatomy & histology* / Q000502:physiology*; D051379:Mice; D012038:Regeneration; D014054:Tomography / Q000379:methods*
Angiogenesis; Euler number; image analysis; mathematical morphology; microcomputed tomography; synchrotron radiation