Datasets:

varora
/

HIT

@@ -39,14 +39,13 @@ tags:
 - **Homepage:** [https://hit.is.tue.mpg.de/](https://hit.is.tue.mpg.de/)
 - **Repository:** [https://github.com/MarilynKeller/HIT](https://github.com/MarilynKeller/HIT)
-- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
 - **Point of Contact:** [Vaibhav Arora](vaibhav.arora@inria.fr), [Marilyn Keller](marilyn.keller@tuebingen.mpg.de), [Sergi Pujades](sergi.pujades-rocamora@inria.fr)
 ### Dataset Summary
 The HIT dataset is a structured dataset of paired observations of body's inner tissues and the body surface. More concretely, it is a dataset of paired full-body volumetric segmented (bones, lean, and adipose tissue) MRI scans and SMPL meshes capturing the body surface shape for male (N=157) and female (N=241) subjects respectively. This is relevant for medicine, sports science, biomechanics, and computer graphics as it can ease the creation of personalized anatomic digital twins that model our bones, lean, and adipose tissue.
 Dataset acquistion: We work with scans acquired with a 1.5 T scanner (Magnetom Sonata, Siemens Healthcare) following a standardized protocol for whole body adipose tissue topography mapping. All subjects gave prior informed written consent and the study was approved by the local ethics board. Each scan has around 110 slices, slightly varying depending on the height of the subject. The slice resolution is 256 × 192, with an approximate voxel size of 2 × 2 × 10 mm. These slices are segmented into bones, lean, and adipose tissue by leveraging initial automatic segmentations and manual annotations to train and refine nnUnets with the help of human supervision. For each subject, we then fit the SMPL body mesh to the surface of the segmented MRI in a manner that captures the flattened shape of subjects in their lying positions on belly in the scanner (refer to Sec 3.2 in main paper for further details). Therefore for each subject, we provide the MRI segmented array and the SMPL mesh faces and vertices (in addition to the SMPL parameters).
-[IMAGE]
 ### Supported Tasks and Leaderboards
 HIT fosters a new direction and therefore there aren't any exisiting Benchmarks. We encourage the use of the dataset to open up new tasks and research directions.

 - **Homepage:** [https://hit.is.tue.mpg.de/](https://hit.is.tue.mpg.de/)
 - **Repository:** [https://github.com/MarilynKeller/HIT](https://github.com/MarilynKeller/HIT)
+- **Paper:** [Coming Soon](Coming Soon)
 - **Point of Contact:** [Vaibhav Arora](vaibhav.arora@inria.fr), [Marilyn Keller](marilyn.keller@tuebingen.mpg.de), [Sergi Pujades](sergi.pujades-rocamora@inria.fr)
 ### Dataset Summary
 The HIT dataset is a structured dataset of paired observations of body's inner tissues and the body surface. More concretely, it is a dataset of paired full-body volumetric segmented (bones, lean, and adipose tissue) MRI scans and SMPL meshes capturing the body surface shape for male (N=157) and female (N=241) subjects respectively. This is relevant for medicine, sports science, biomechanics, and computer graphics as it can ease the creation of personalized anatomic digital twins that model our bones, lean, and adipose tissue.
 Dataset acquistion: We work with scans acquired with a 1.5 T scanner (Magnetom Sonata, Siemens Healthcare) following a standardized protocol for whole body adipose tissue topography mapping. All subjects gave prior informed written consent and the study was approved by the local ethics board. Each scan has around 110 slices, slightly varying depending on the height of the subject. The slice resolution is 256 × 192, with an approximate voxel size of 2 × 2 × 10 mm. These slices are segmented into bones, lean, and adipose tissue by leveraging initial automatic segmentations and manual annotations to train and refine nnUnets with the help of human supervision. For each subject, we then fit the SMPL body mesh to the surface of the segmented MRI in a manner that captures the flattened shape of subjects in their lying positions on belly in the scanner (refer to Sec 3.2 in main paper for further details). Therefore for each subject, we provide the MRI segmented array and the SMPL mesh faces and vertices (in addition to the SMPL parameters).
+![alt text](extras/hit_dataset.png)
 ### Supported Tasks and Leaderboards
 HIT fosters a new direction and therefore there aren't any exisiting Benchmarks. We encourage the use of the dataset to open up new tasks and research directions.