DeformedTissue Dataset

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.

组织形变（Tissue deformation）是软组织手术中的一项关键挑战，尤其在肿瘤切除过程中，该问题会引发解剖标记点移位，进而干扰组织定位。本研究针对45头猪头部尸体标本开展实验以模拟组织形变，相关实验已通过曼海姆兽医办公室（Mannheim Veterinary Office，DE 08 222 1019 21）审批。
研究团队采用3D相机与头戴式显示器，采集经加热诱导的可控形变前后的组织形态。随后通过Meshroom、MeshLab及Blender等软件对数据进行处理，以构建并评估二维半网格（2½D meshes）。
本数据集包含不同程度的形变、噪声与离群点（outliers），其生成方法与SynBench数据集保持一致。
1. 形变等级（Deformation_Level）：
共设置10种形变等级，其中0.1与0.7分别代表最小与最大形变程度。每个文件夹内均包含源文件与目标文件，形变过程仅作用于目标文件。为简化使用，与目标文件对应的源文件将以相同名称存放于该文件夹中，其中源文件以“Source_”为前缀，目标文件以“Target_”为前缀。“Source_”与“Target_”后的数字对应“Data”文件夹中的原始对象编号，例如“Target_3”表示该文件由“Data”文件夹中的3号原始对象生成。文件名中剩余的两个数字分别代表控制点百分比与高斯径向基函数（Gaussian radial basis function）的宽度参数。
2. 带噪声数据（Noisy_Data）：
针对“形变等级”文件夹下所有形变等级的可用文件，生成带噪声的数据。噪声共分为4个等级，分别为0.01、0.02、0.03与0.04（具体实现细节详见论文）。
文件名与“形变等级”文件夹中的对应文件完全一致。
3. 带离群点数据（Outlier_Data）：
针对“形变等级”文件夹下所有形变等级的可用文件，生成包含离群点的数据。离群点共分为5个等级类别，分别为5%、15%、25%、35%与45%（具体实现细节详见论文）。
文件名与“形变等级”文件夹中的对应文件完全一致。此外，每个文件会附带一个同名的额外文件，前缀为“Outlier_”，该文件为包含离群点坐标的矩阵，可作为基准数据集用于验证后续算法的有效性。
附加说明：考虑到所有挑战样本均生成于从小至大的形变等级区间内，本DeformedTissue数据集可支持用户根据所提出方法的性能需求选取对应数据，以验证其方法面对复杂挑战时的鲁棒性。