Jianlong Zhou

Topology polymorphism graph for lung tumor segmentation in PET-CT images

Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a topo-poly graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an isolated group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a complex group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dices similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881 ± 0.046 and HD of 5.311 ± 3.022 mm for the isolated cases and DSC of 0.870 ± 0.038 and HD of 9.370 ± 3.169 mm for the complex cases. Students t-test showed that our model outperformed the other methods (p-values <0.05).

Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint

PurposeAccurate lung tumor segmentation is a prerequisite for effective radiation therapy and surgical planning. However, tumor delineation is challenging when the tumor boundaries are indistinct on PET or CT. To address this problem, we developed a segmentation method to improve the delineation of primary lung tumors from PET–CT images.MethodsWe formulated the segmentation problem as a label information propagation process in an iterative manner. Our model incorporates spatial–topological information from PET and local intensity changes from CT. The topological information of the regions was extracted based on the metabolic activity of different tissues. The spatial–topological information moderates the amount of label information that a pixel receives: The label information attenuates as the spatial distance increases and when crossing different topological regions. Thus, the spatial–topological constraint assists accurate tumor delineation and separation. The label information propagation and transition model are solved under a random walk framework.ResultsOur method achieved an average DSC of

Topology constraint graph-based model for non-small-cell lung tumor segmentation from PET volumes

0.848 pm 0.036

Trust and Reliance Based on System Accuracy

0.848±0.036 and HD (mm) of

Importance-aware lighting design in volume visualization

8.652 pm 4.532

BVP Feature Signal Analysis for Intelligent User Interface

8.652±4.532 on 40 patients with lung cancer. The t test showed a significant improvement (p valuexa0