TY - JOUR
T1 - TopoAngler
T2 - Interactive Topology-Based Extraction of Fishes
AU - Bock, Alexander
AU - Doraiswamy, Harish
AU - Summers, Adam
AU - Silva, Cláudio
N1 - Funding Information:
This work was supported primarily by the Moore-Sloan Data Science Environment at New York University and The Seaver Institute. Further support provided by NASA, DOE, and NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, and TCN-1701665. We would further like to thank the anonymous reviewers that helped improving the quality of this paper. The described concepts have been realized using the Inviwo visualization framework (www.inviwo.org).
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2017
Y1 - 2017
N2 - We present TopoAngler, a visualization framework that enables an interactive user-guided segmentation of fishes contained in a micro-CT scan. The inherent noise in the CT scan coupled with the often disconnected (and sometimes broken) skeletal structure of fishes makes an automatic segmentation of the volume impractical. To overcome this, our framework combines techniques from computational topology with an interactive visual interface, enabling the human-in-the-Ioop to effectively extract fishes from the volume. In the first step, the join tree of the input is used to create a hierarchical segmentation of the volume. Through the use of linked views, the visual interface then allows users to interactively explore this hierarchy, and gather parts of individual fishes into a coherent sub-volume, thus reconstructing entire fishes. Our framework was primarily developed for its application to CT scans of fishes, generated as part of the ScanAllFish project, through close collaboration with their lead scientist. However, we expect it to also be applicable in other biological applications where a single dataset contains multiple specimen; a common routine that is now widely followed in laboratories to increase throughput of expensive CT scanners.
AB - We present TopoAngler, a visualization framework that enables an interactive user-guided segmentation of fishes contained in a micro-CT scan. The inherent noise in the CT scan coupled with the often disconnected (and sometimes broken) skeletal structure of fishes makes an automatic segmentation of the volume impractical. To overcome this, our framework combines techniques from computational topology with an interactive visual interface, enabling the human-in-the-Ioop to effectively extract fishes from the volume. In the first step, the join tree of the input is used to create a hierarchical segmentation of the volume. Through the use of linked views, the visual interface then allows users to interactively explore this hierarchy, and gather parts of individual fishes into a coherent sub-volume, thus reconstructing entire fishes. Our framework was primarily developed for its application to CT scans of fishes, generated as part of the ScanAllFish project, through close collaboration with their lead scientist. However, we expect it to also be applicable in other biological applications where a single dataset contains multiple specimen; a common routine that is now widely followed in laboratories to increase throughput of expensive CT scanners.
KW - Computational topology
KW - branch decomposition
KW - hierarchical segmentation
KW - interaction
KW - join trees
KW - visualization system
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U2 - 10.1109/TVCG.2017.2743980
DO - 10.1109/TVCG.2017.2743980
M3 - Article
C2 - 28866509
AN - SCOPUS:85028697484
SN - 1077-2626
VL - 24
SP - 812
EP - 821
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 1
M1 - 8017639
ER -