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--- |
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license: mit |
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--- |
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# Dataset Card for DenSpine |
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## Volumetric Files |
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The dataset is comprised of dendrites from 3 brain samples: `seg_den` (also known as `M50`), `mouse` (`M10`), and `human` (`H10`). |
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Every species has 3 volumetric `.h5` files: |
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- `{species}_raw.h5`: instance segmentation of entire dendrites in volume (labelled `1-50` or `1-10`), where trunks and spines share the same label |
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- `{species}_spine.h5`: "binary" segmentation, where trunks are labelled `0` and spines are labelled their `raw` dendrite label |
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- `{species}_seg.h5`: spine instance segmentation (labelled `51-...` or `11-...`), where every spine in the volume is labelled uniquely |
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## Point Cloud Files |
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In addition, we provide preprocessed point clouds sampled along a dendrite's centerline skeletons for ease of use in evaluating point-cloud based methods. |
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```python |
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data=np.load(f"{species}_1000000_10000/{idx}.npz", allow_pickle=True) |
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trunk_id, pc, trunk_pc, label = data["trunk_id"], data["pc"], data["trunk_pc"], data["label"] |
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``` |
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- `trunk_id` is an integer which corresponds to the dendrite's `raw` label |
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- `pc` is a shape `[1000000,3]` isotropic point cloud |
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- `trunk_pc` is a shape `[skeleton_length, 3]` (ordered) array, which represents the centerline of the trunk of `pc` |
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- `label` is a shape `[1000000]` array with values corresponding to the `seg` labels of each point in the point cloud |
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We provide a comprehensive example of how to instantiate a PyTorch dataloader using our dataset in `dataloader.py` (potentially using the FFD transform with `frenet=True`). |
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## Training splits for `seg_den` |
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The folds used for training/evaluating the `seg_den` dataset, based on `raw` labels are defined as follows: |
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```python |
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seg_den_folds = [ |
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[3, 5, 11, 12, 23, 28, 29, 32, 39, 42], |
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[8, 15, 19, 27, 30, 34, 35, 36, 46, 49], |
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[9, 14, 16, 17, 21, 26, 31, 33, 43, 44], |
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[2, 6, 7, 13, 18, 24, 25, 38, 41, 50], |
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[1, 4, 10, 20, 22, 37, 40, 45, 47, 48], |
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] |
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``` |
