. Utilizing a Micro-CT scan containing nine distinct fishes (left) in a topological analysis results in a number of candidate features that correspond to entire or partial fishes (center). By enabling the user to select candidate features at different simplification levels, individual fishes can be interactively segmented (right). Abstract-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-loop 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.
Directly related to CT scans of fishes, in our previous work we proposed TopoAngler REF that combines a topology based approach with a visual framework to help users segment fishes from the CT data.
TopoAngler: Interactive Topology-Based Extraction of Fishes
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine