first README.md

README.md

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