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WEISS Open Data Server Beta


WEISS Open Data Server is a service to promote WEISS published datasets. WEISS believes in Open and Reproducible research and therefore the datasets which does not have any sensitive data are open to access and available to reuse.

It also hosts relevant external datasets that could benefit WEISS researchers.

Do you have a published dataset that you would like to promote or do you know an external dataset that could benefit WEISS researchers? If yes, then please let us know and we will add it here.


WEISS Internal Datasets:

Preview Description Web Ref Dataset Size
Feto placenta data preview The dataset contains 483 frames with ground-truth vessel segmentation annotations taken from six different in vivo fetoscopic procedure videos. The dataset also includes six unannotated in vivo continuous fetoscopic video clips (950 frames) with predicted vessel segmentation maps obtained from the leave-one-out cross validation reported in [Bano et al. MICCAI2020]. URL [1] Fetoscopy Placenta Dataset
450 MB
Synthetic data preview The dataset consists of 14 + 6 videos of 300 frames each, with corresponding segmentation ground truth and robot kinematic data. Frames were initially recorded at 720x576 pixel resolution and then centrally cropped (538x701) to remove side camera artefacts. The employed technique to produce segmentation labels, as well as further details on our dataset, are accurately described in our [Emanuele et al. MICCAI2020]. URL [2] Synthetic Dataset 3.5 GB
Synthetic Colonoscopy Dataset preview Synthetic colonoscopy dataset consists of 16,016 RGB images with corresponding ground truth depth. The images are based on a human CTC and were generated in Unity. The depth is scaled to [0,1] which corresponds to [0,20] cm. The data is divided into groups according to its texture (T1, T2, T3) and the lighting (L1, L2, L3). For each configuration there are four to five different subsets generated by randomly shifting and rotating the virtual camera. Please see website and publication for more information. For access permissions, click contact. URL [3] Colonoscopy Dataset 1.2 GB
SERV-CT preview This dataset consists of 16 rectified stereoendoscopic 720 x 576 resolution images of two porcine ex vivo full torso samples. CT of both endoscope and anatomy enable constrained manual alignment producing reference disparity, depth and occlusion maps for each stereo pair. These are intended for validation of surgical stereo reconstruction algorithms. Reference to follow shortly. URL [4] SERV-CT 407 MB
Semio2Brain v.1.2.2 Descriptive Seizure Semiologies and their multi-one-hot encoded categorical hierarchical brain localisations (11230 datapoints) and lateralisations (2391 datapoints) from 4643 patients across 309 included articles, as a Microsoft Excel Spreadsheet. doi:10.5281/zenodo.4473240 URL Semio2Brain 735 KB




External Datasets:

Preview Description Ref Web
Discover research from Wellcome Trust A webspace dedicated to Wellcome Trust-funded researchers to upload the research datasets and relevant materials to support open research and reproducibility. URL
Validating and Benchmarking Computer Assisted Surgery Open-CAS is an open collection of datasets for validating and benchmarking computer-assisted surgery systems. The data collections are updated constantly. URL
Laparoscopic Image to Image Translation The dataset is split into multiple archives. In each archive, you will find subfolders for each patient. Folder names are the patient pseudonyms (pseudonyms taken from the original IRCAD 3D CT liver data set which was used to generate the 3D scenes). [5] URL
Grand Challenge Grand Challenge provides a platform for end-to-end development of machine learning solutions in biomedical imaging. It helps to manage your data, train expert annotators, gather annotations, benchmark algorithms and deploy the algorithms. URL
Health Data Research Innovation Gateway The Health Data Research Innovation Gateway (the ‘Gateway’) provides a common entry point to discover and enquire about access to UK health datasets for research and innovation. It provides detailed information about the datasets, which are held by members of the UK Health Data Research Alliance, such as a description, size of the population, and the legal basis for access. URL
Keggle Dataset Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time. URL
UCI Dataset We currently maintain 559 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. For a general overview of the Repository, please visit our About page. [6] URL


References:

  1. Bano, S., Vasconcelos, F., Shepherd, L.M., Vander Poorten, E., Vercauteren, T., Ourselin, S., David, A.L., Deprest, J. and Stoyanov, D., 2020, October. Deep placental vessel segmentation for fetoscopic mosaicking. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 763-773). Springer, Cham.
  2. Colleoni, E., Edwards, P. and Stoyanov, D., 2020, October. Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 700-710). Springer, Cham.
  3. Rau, A., Edwards, P.E., Ahmad, O.F., Riordan, P., Janatka, M., Lovat, L.B. and Stoyanov, D., 2019. Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy. International journal of computer assisted radiology and surgery, 14(7), pp.1167-1176.
  4. Edwards, P.J., Psychogyios, D., Speidel, S., Maier-Hein, L. and Stoyanov, D., 2020. SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction. arXiv preprint arXiv:2012.11779.
  5. Pfeiffer, M., Funke, I., Robu, M.R., Bodenstedt, S., Strenger, L., Engelhardt, S., Roß, T., Clarkson, M.J., Gurusamy, K., Davidson, B.R. and Maier-Hein, L., 2019, October. Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 119-127). Springer, Cham.
  6. Dua, D. and Graff, C., 2019. UCI machine learning repository, 2017. URL http://archive. ics. uci. edu/ml, 37.




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