TY - GEN
T1 - Three-dimensional shape and topology analysis of tissue-cleared tumor samples
AU - Poinapen, Danny
AU - Yoshizawa, Tadashi
AU - Zho, Yuan
AU - Charon, Nicholas
AU - Mou, Stephanie
AU - Oshima, Kiyoko
AU - Wood, Laura
AU - Hruban, Ralph H.
AU - Zbijewski, Wojciech
N1 - Funding Information:
The current work has not been submitted for publication or presentation elsewhere, and it is supported by the generous grant from the Sol Goldman Pancreatic Cancer Research Center.
Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021
Y1 - 2021
N2 - Purpose: We developed a semi-automated framework to obtain numerical descriptors of surface morphology and topology from volumetric microscopy of human cleared cancer tissues to enable quantitative studies of 3D tumor microarchitecture. Methods: Individual slices of immunolabeled confocal or light-sheet microscopic images of cleared cancer tissue samples are first segmented using the Chan-Vese morphological snake method. Then, the Marching Cubes algorithm is used to generate 3D models of the tumors. Surface area-volume ratio (SAV) of the 3D models is computed using the discrete divergence theorem. Geometries of model centerlines (obtained as shortest paths of maximal inscribed spheres) are quantified in terms of their curvature, torsion, and bifurcations angles. Topological analysis is performed on 3D point clouds generated by uniformly sampling the 3D models. Vietoris-Rips (VR) simplicial complexes of the point clouds are constructed, and their persistent diagrams are used to compute the lifetime of homological features such as connected components, loops, and voids. The framework is applied to cleared samples of extrahepatic cholangiocarcinoma labeled with CK19. Specifically, we investigate whether the proposed quantitative descriptors of tumor microarchitecture can differentiate cancers showing low-grade (LG) tumor budding (TB) from those presenting high-grade (HG) TB. Results: The proposed framework yielded 3D surface models of the tumors that retained the major morphological features (e.g., glands and protrusions) observable in the microscopic image stacks. Initial evidence from quantitative analysis of the 3D models (3 samples each of HG and LG tumors) indicates quantitative differences in the microarchitecture of HG and LG cancer tissues. The average SAV ratio of HG tumors was 0.153±0.0036 μm-1 compared to 0.235±0.0089 μm-1 for LG samples. Analysis of centerline geometries found less curvature in HG samples compared to LG (average curvature of 15.87±0.122 mm-1 vs. 20.87±0.122 mm-1), less torsion (51.54±1.077 mm-1 vs. 62.73±1.120 mm-1), and narrower bifurcation angles (0.543±0.0303 rads vs. 0.671±0.0281 rads). Persistent homology, via VR filtration, indicated that the connected components (homological dimension H0) have longer lifetime in LG tumors (mean lifetime 0.0349 ±0.00297) than in HG ones (mean lifetime 0.0284 ±0.00307). Conclusion: The proposed quantitative analysis framework yields potential geometrical and topological descriptors for statistical analysis of the rich 3D imaging data made available by the application of tissue clearing to human tumor samples.
AB - Purpose: We developed a semi-automated framework to obtain numerical descriptors of surface morphology and topology from volumetric microscopy of human cleared cancer tissues to enable quantitative studies of 3D tumor microarchitecture. Methods: Individual slices of immunolabeled confocal or light-sheet microscopic images of cleared cancer tissue samples are first segmented using the Chan-Vese morphological snake method. Then, the Marching Cubes algorithm is used to generate 3D models of the tumors. Surface area-volume ratio (SAV) of the 3D models is computed using the discrete divergence theorem. Geometries of model centerlines (obtained as shortest paths of maximal inscribed spheres) are quantified in terms of their curvature, torsion, and bifurcations angles. Topological analysis is performed on 3D point clouds generated by uniformly sampling the 3D models. Vietoris-Rips (VR) simplicial complexes of the point clouds are constructed, and their persistent diagrams are used to compute the lifetime of homological features such as connected components, loops, and voids. The framework is applied to cleared samples of extrahepatic cholangiocarcinoma labeled with CK19. Specifically, we investigate whether the proposed quantitative descriptors of tumor microarchitecture can differentiate cancers showing low-grade (LG) tumor budding (TB) from those presenting high-grade (HG) TB. Results: The proposed framework yielded 3D surface models of the tumors that retained the major morphological features (e.g., glands and protrusions) observable in the microscopic image stacks. Initial evidence from quantitative analysis of the 3D models (3 samples each of HG and LG tumors) indicates quantitative differences in the microarchitecture of HG and LG cancer tissues. The average SAV ratio of HG tumors was 0.153±0.0036 μm-1 compared to 0.235±0.0089 μm-1 for LG samples. Analysis of centerline geometries found less curvature in HG samples compared to LG (average curvature of 15.87±0.122 mm-1 vs. 20.87±0.122 mm-1), less torsion (51.54±1.077 mm-1 vs. 62.73±1.120 mm-1), and narrower bifurcation angles (0.543±0.0303 rads vs. 0.671±0.0281 rads). Persistent homology, via VR filtration, indicated that the connected components (homological dimension H0) have longer lifetime in LG tumors (mean lifetime 0.0349 ±0.00297) than in HG ones (mean lifetime 0.0284 ±0.00307). Conclusion: The proposed quantitative analysis framework yields potential geometrical and topological descriptors for statistical analysis of the rich 3D imaging data made available by the application of tissue clearing to human tumor samples.
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U2 - 10.1117/12.2582601
DO - 10.1117/12.2582601
M3 - Conference contribution
AN - SCOPUS:85103238529
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
PB - SPIE
T2 - Medical Imaging 2021: Digital Pathology
Y2 - 15 February 2021 through 19 February 2021
ER -