DeformedTissue dataset - Monji Azad, Sara; Scherl, Claudia; Männle, David (Universität, 2025)
Tissue deformation is a critical issue in soft-tissue surgery, particularly during tumor resection, as it causes landmark displacement, complicating tissue orientation. The authors conducted an experimental study on 45 pig head cadavers to simulate tissue deformation, approved by the Mannheim Veterinary Office (DE 08 222 1019 21). We used 3D cameras and head-mounted displays to capture tissue shapes before and after controlled deformation induced by heating. The data were processed using software such as Meshroom, MeshLab, and Blender to create and evaluate 2½D meshes. The dataset includes different levels of deformation, noise, and outliers, generated using the same approach as the SynBench dataset. 1. Deformation_Level: 10 different deformation levels are considered. 0.1 and 0.7 are representing minimum and maximum deformation, respectively. Source and target files are available in each folder. The deformation process is just applied to target files. For simplicity, the corresponding source files to the target ones are available in this folder with the same name, but source ones start with Source_ and the target files start with Target_. The number after Source_ and Target_ represents the primitive object in the “Data” folder. For example, Target_3 represents that this file is generated from object number 3 in the “Data” folder. The two other numbers in the file name represent the percentage number of control points and the width of the Gaussian radial basis function, respectively. 2. Noisy_Data For all available files in the “Deformation_Level” folder (for all deformation levels), Noisy data is generated. They are generated in 4 different noise levels namely, 0.01, 0.02, 0.03, and 0.04 (More explanation about implementation can be found in the paper). The name of the files is the same as the files in the “Deformation_Level” folder. 3. Outlier_Data For all available files in the “Deformation_Level” folder (for all deformation levels), data with outliers is generated. They are generated in different outlier levels, in 5 categories, namely, 5%, 15%, 25%, 35%, and 45% (More explanation about implementation can be found in the paper). The name of the files is the same as the files in the “Deformation_Level” folder. Furthermore, for each file, there is one additional file with the same name but is started with “Outlier_”. This represents a matrix with the coordinates of outliers. Then, it would be possible to use these files as benchmarks to check the validity of future algorithms. Additional notes: Considering the fact that all challenges are generated under small to large deformation levels, the DeformedTissue dataset makes it possible for users to select their desired data based on the ability of their proposed method, to show how robust to complex challenges their methods are.