Update datasets_and_dataloaders.md (#387)

Author: anwai98

doc/datasets_and_dataloaders.md

@@ -8,6 +8,7 @@ They are implemented in `torch_em.data.datasets`. See `scripts/datasets` for exa
 
 All datasets in `torch_em.data.datasets` are implemented according to the following logic:
 - The function `get_..._data` downloads the respective datasets. Note that some datasets cannot be downloaded automatically. In these cases the function will raise an error with a message that explains how to download the data.
+- The function `get_..._paths` returns the filepaths to the downloaded inputs.
 - The function `get_..._dataset` returns the PyTorch Dataset for the corresponding dataset.
 - The function `get_..._dataloader` returns the PyTorch DataLoader for the corresponding dataset.
 
@@ -21,11 +22,11 @@ We provide several electron microscopy datasets. See `torch_em.data.datasets.ele
 
 ### Histopathology 
 
-`torch_em.data.datasets.histopathology`
+We provide several histopathology datasets. See `torch_em.data.datasets.histopathology` for an overview.
 
 ### Medical Imaging
 
-`torch_em.data.datasets.medical`
+We provide several medical imaging datasets. See `torch_em.data.datasets.medical` for an overview.
 
 
 ## How to create your own dataloader?
@@ -48,9 +49,11 @@ Let's say you have a specific dataset of interest and would want to create a PyT
         - ✅ different sizes (i.e. images have shapes like (256, 256), (378, 378), (512, 512), etc., for example)
             - use `ImageCollectionDataset`
     - Multi-channel inputs of:
-        - > NOTE: It's important to convert the images to be channels first (see above for the expected format)
-        - ✅ same size (i.e. all images have shape (3, 256, 256), for example)
-            - use `SegmentationDataset` (recommended) or `ImageCollectionDataset`
+        - > The ideal expectation of inputs with channels is to have channels first (eg. RGB format -> (256, 256, 3) to channels-first format -> (3, 256, 256))
+        - CASE 1: I would like to keep the inputs as RGB format (you must stick to `ImageCollectionDataset` or `is_seg_dataset=False`)
+        - CASE 2: I would like to convert the inputs to channels-first (you can be flexible and follow the instructions below)
+        - ✅ same size (i.e. all images have shape)
+            - use `SegmentationDataset` (recommended for inputs with channels first) or `ImageCollectionDataset` (for inputs in RGB format)
         - ✅ different sizes (i.e. images have shapes like (3, 256, 256), (3, 378, 378), (3, 512, 512), etc., for example)
             - use `ImageCollectionDataset`
 
@@ -135,51 +138,3 @@ dataset = RawImageCollectionDataset(
     # there are other optional parameters, see `torch_em.data.raw_image_collection_dataset.py` for details.
 )
 ```
-
-
-<!-- I would rather auto-generate this in a proper pdoc documentation.
-- ASEM (`asem.py`): Segmentation of organelles in FIB-SEM cells.
-- AxonDeepSeg (`axondeepseg.py`): Segmentation of myelinated axons in electron microscopy.
-- MitoLab* (`cem.py`):
-    - CEM MitoLab: Segmentation of mitochondria in electron microscopy.
-    - CEM Mito Benchmark: Segmentation of mitochondria in 7 benchmark electron microscopy datasets.
-- Covid IF (`covidif.py`): Segmentation of cells and nuclei in immunofluoroscence.
-- CREMI (`cremi.py`): Segmentation of neurons in electron microscopy.
-- Cell Tracking Challenge (`ctc.py`): Segmentation data for cell tracking challenge (consists of 10 datasets).
-- DeepBacs (`deepbacs.py`): Segmentation of bacteria in light microscopy.
-- DSB (`dsb.py`): Segmentation of nuclei in light microscopy.
-- DynamicNuclearNet* (`dynamicnuclearnet.py`): Segmentation of nuclei in fluorescence microscopy.
-- HPA (`hpa.py`): Segmentation of cells in light microscopy.
-- ISBI (`isbi2012.py`): Segmentation of neurons in electron microscopy.
-- Kasthuri (`kasthuri.py`): Segmentation of mitochondria in electron microscopy.
-- LIVECell (`livecell.py`): Segmentation of cells in phase-contrast microscopy.
-- Lucchi (`lucchi.py`): Segmentation of mitochondria in electron microscopy.
-- MitoEM (`mitoem.py`): Segmentation of mitochondria in electron microscopy.
-- Mouse Embryo (`mouse_embryo.py`): Segmentation of nuclei in confocal microscopy.
-- NeurIPS CellSeg (`neurips_cell_seg.py`): Segmentation of cells in multi-modality light microscopy datasets.
-- NucMM (`nuc_mm.py`): Segmentation of nuclei in electron microscopy and micro-CT.
-- PlantSeg (`plantseg.py`): Segmentation of cells in confocal and light-sheet microscopy.
-- Platynereis (`platynereis.py`): Segmentation of nuclei in electron microscopy.
-- PNAS* (`pnas_arabidopsis.py`): TODO
-- SNEMI (`snemi.py`): Segmentation of neurons in electron microscopy.
-- Sponge EM (`sponge_em.py`): Segmentation of sponge cells and organelles in electron microscopy.
-- TissueNet* (`tissuenet.py`): Segmentation of cellls in tissue imaged with light microscopy.
-- UroCell (`uro_cell.py`): Segmentation of mitochondria and other organelles in electron microscopy.
-- VNC (`vnc.py`): Segmentation of mitochondria in electron microscopy
-
-### Histopathology
-
-- BCSS (`bcss.py`): Segmentation of breast cancer tissue in histopathology.
-- Lizard* (`lizard.py`): Segmentation of nuclei in histopathology.
-- MoNuSaC (`monusac.py`): Segmentation of multi-organ nuclei in histopathology.
-- MoNuSeg (`monuseg.py`): Segmentation of multi-organ nuclei in histopathology.
-- PanNuke (`pannuke.py`): Segmentation of nuclei in histopathology.
-
-### Medical Imaging
-
-- AutoPET* (`medical/autopet.py`): Segmentation of lesions in whole-body FDG-PET/CT.
-- BTCV* (`medical/btcv.py`): Segmentation of multiple organs in CT.
-
-### NOTE:
-- \* - These datasets cannot be used out of the box (mostly because of missing automatic downloading). Please take a look at the scripts and the dataset object for details.
--->