The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm. Usage Notes The datasets are formatted in the HDF5 format developed by the HDF5 Group. We utilized and thus recommend the usage of Python bindings pyHDF to handle the datasets. The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets: raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2 Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets. Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows: {'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])} {'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])} {'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}

This research project received support from the Bavarian Ministry of Economic Affairs, Infrastructure, Transport and Technology as well as from the Interdisziplinäres Zentrum für Klinische Forschung der Universität Würzburg, Grant Z_4/150-200. D.M. Pelt is supported by The Netherlands Organization for Scientific Research (NWO), project number 016.Veni.192.235