TY - GEN
T1 - Real-time spatial registration for 3D human atlas
AU - Chen, Lu
AU - Teng, Dejun
AU - Zhu, Tian
AU - Kong, Jun
AU - Herr, Bruce W.
AU - Bueckle, Andreas
AU - Börner, Katy
AU - Wang, Fusheng
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - The human body is made up of about 37 trillion cells (adults). Each cell has its own unique role and is affected by its neighboring cells and environment. The NIH Human BioMolecular Atlas Program (HuBMAP) aims at developing a 3D atlas of human body consisting of organs, vessels, tissues to singe cells with all 3D spatially registered in a single 3D human atlas using tissues obtained from normal individuals across a wide range of ages. A critical step of building the atlas is to register 3D tissue blocks in real-time to the right location of a human organ, which itself consists of complex 3D sub-structures. The complexity of the 3D organ model, e.g., 35 meshes for a typical kidney, poses a significant computational challenge for the registration. In this paper, we propose a comprehensive framework TICKET (TIssue bloCK rEgisTration) to support tissue block registration for 3D human atlas, including (1) 3D mesh pre-processing, (2) spatial queries on intersection relationship and (3) intersection volume computation between organs and tissue blocks. To minimize search space and computation cost, we develop multi-level indexing at both the anatomical structure level and mesh level, and utilize OpenMP for parallel computing. Considering cuboid based shape of the tissue block, we propose an efficient voxelization-based method to estimate the intersection volume. Our experiments demonstrate that the proposed framework is efficient and practical. TICKET is being integrated into the HuBMAP CCF registration portal [1].
AB - The human body is made up of about 37 trillion cells (adults). Each cell has its own unique role and is affected by its neighboring cells and environment. The NIH Human BioMolecular Atlas Program (HuBMAP) aims at developing a 3D atlas of human body consisting of organs, vessels, tissues to singe cells with all 3D spatially registered in a single 3D human atlas using tissues obtained from normal individuals across a wide range of ages. A critical step of building the atlas is to register 3D tissue blocks in real-time to the right location of a human organ, which itself consists of complex 3D sub-structures. The complexity of the 3D organ model, e.g., 35 meshes for a typical kidney, poses a significant computational challenge for the registration. In this paper, we propose a comprehensive framework TICKET (TIssue bloCK rEgisTration) to support tissue block registration for 3D human atlas, including (1) 3D mesh pre-processing, (2) spatial queries on intersection relationship and (3) intersection volume computation between organs and tissue blocks. To minimize search space and computation cost, we develop multi-level indexing at both the anatomical structure level and mesh level, and utilize OpenMP for parallel computing. Considering cuboid based shape of the tissue block, we propose an efficient voxelization-based method to estimate the intersection volume. Our experiments demonstrate that the proposed framework is efficient and practical. TICKET is being integrated into the HuBMAP CCF registration portal [1].
KW - 3D spatial queries
KW - human atlas
KW - parallel computing
KW - spatial indexing
UR - https://www.scopus.com/pages/publications/85142621956
U2 - 10.1145/3557917.3567618
DO - 10.1145/3557917.3567618
M3 - Conference contribution
AN - SCOPUS:85142621956
T3 - Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022
SP - 27
EP - 35
BT - Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022
A2 - Shashidharan, Ashwin
A2 - Gadiraju, Krishna Karthik
A2 - Chandola, Varun
A2 - Vatsavai, Ranga Raju
PB - Association for Computing Machinery, Inc
T2 - 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022
Y2 - 1 November 2022
ER -