Minor update to environment file and other docs (#972)

Minor update to environment file and docs
---------

Co-authored-by: Constantin Pape <[email protected]>

Author: anwai98

README.md

@@ -19,7 +19,7 @@ We implement napari applications for:
 <img src="https://github.com/computational-cell-analytics/micro-sam/assets/4263537/dfca3d9b-dba5-440b-b0f9-72a0683ac410" width="256">
 <img src="https://github.com/computational-cell-analytics/micro-sam/assets/4263537/aefbf99f-e73a-4125-bb49-2e6592367a64" width="256">
 
-If you run into any problems or have questions regarding our tool please open an issue on Github or reach out via [image.sc](https://forum.image.sc/) using the tag `micro-sam` and tagging @constantinpape.
+If you run into any problems or have questions regarding our tool please open an [issue](https://github.com/computational-cell-analytics/micro-sam/issues/new/choose) on Github or reach out via [image.sc](https://forum.image.sc/) using the tag `micro-sam`, and tagging [@constantinpape](https://forum.image.sc/u/constantinpape/summary) and [@anwai98](https://forum.image.sc/u/anwai98/summary).
 
 
 ## Installation and Usage
@@ -31,7 +31,7 @@ Please check [the documentation](https://computational-cell-analytics.github.io/
 
 We welcome new contributions!
 
-If you are interested in contributing to micro-sam, please see the [contributing guide](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#contribution-guide). The first step is to [discuss your idea in a new issue](https://github.com/computational-cell-analytics/micro-sam/issues/new) with the current developers.
+If you are interested in contributing to `micro-sam`, please see the [contributing guide](https://computational-cell-analytics.github.io/micro-sam/micro_sam.html#contribution-guide). The first step is to [discuss your idea in a new issue](https://github.com/computational-cell-analytics/micro-sam/issues/new) with the current developers.
 
 
 ## Citation
@@ -50,12 +50,12 @@ There are a few other napari plugins build around Segment Anything:
 - https://github.com/hiroalchem/napari-SAM4IS
 
 Compared to these we support more applications (2d, 3d and tracking), and provide finetuning methods and finetuned models for microscopy data.
-[WebKnossos](https://webknossos.org/) also offers integration of SegmentAnything for interactive segmentation.
+[WebKnossos](https://webknossos.org/) and [QuPath](https://qupath.github.io/) also offer integration of Segment Anything for interactive segmentation.
 
 We have also built follow-up work that is based on `micro_sam`:
-- https://github.com/computational-cell-analytics/patho-sam - improves SAM for histopathology
-- https://github.com/computational-cell-analytics/medico-sam - improves it for medical imaging
-- https://github.com/computational-cell-analytics/peft-sam - studies parameter efficient fine-tuning for SAM
+- https://github.com/computational-cell-analytics/patho-sam - improves SAM for histopathology.
+- https://github.com/computational-cell-analytics/medico-sam - improves SAM for medical imaging.
+- https://github.com/computational-cell-analytics/peft-sam - studies parameter efficient fine-tuning for SAM.
 
 ## Release Oveverview
 

doc/start_page.md

@@ -32,7 +32,7 @@ After installing `micro_sam`, you can start napari from within your environment
 ```bash
 $ napari
 ```
-After starting napari, you can select the annotation tool you want to use from `Plugins -> SegmentAnything for Microscopy`. Check out the [quickstart tutorial video](https://youtu.be/gcv0fa84mCc) for a short introduction, the video of our [virtual I2K tutorial](https://www.youtube.com/watch?v=dxjU4W7bCis&list=PLdA9Vgd1gxTbvxmtk9CASftUOl_XItjDN&index=33) for an in-depth explanation and [the annotation tool section](#annotation-tools) for details.
+After starting napari, you can select the annotation tool you want to use from `Plugins -> Segment Anything for Microscopy`. Check out the [quickstart tutorial video](https://youtu.be/gcv0fa84mCc) for a short introduction, the video of our [virtual I2K tutorial](https://www.youtube.com/watch?v=dxjU4W7bCis&list=PLdA9Vgd1gxTbvxmtk9CASftUOl_XItjDN&index=33) for an in-depth explanation and [the annotation tool section](#annotation-tools) for details.
 
 The `micro_sam` python library can be imported via
 

environment.yaml

@@ -5,7 +5,7 @@ dependencies:
     - nifty >=1.2.3
     - imagecodecs
     - magicgui
-    - napari >=0.5.0
+    - napari >=0.5.0,<0.6.0
     - natsort
     - pip
     - pooch
@@ -23,7 +23,7 @@ dependencies:
     - pytorch >=2.5
     - segment-anything
     - torchvision
-    - torch_em >=0.7.7
+    - torch_em >=0.7.8
     - tqdm
     - timm
     - xarray <2025.3.0

examples/use_as_library/instance_segmentation.py

@@ -18,10 +18,10 @@ def cell_segmentation():
     predictor = util.get_sam_model()
     embeddings = util.precompute_image_embeddings(predictor, image, save_path=embedding_path)
 
-    # Use the instance segmentation logic of SegmentAnything.
+    # Use the instance segmentation logic of Segment Anything.
     # This works by covering the image with a grid of points, getting the masks for all the poitns
     # and only keeping the plausible ones (according to the model predictions).
-    # While the functionality here does the same as the implementation from SegmentAnything,
+    # While the functionality here does the same as the implementation from Segment Anything,
     # we enable changing the hyperparameters, e.g. 'pred_iou_thresh', without recomputing masks and embeddings,
     # to support (interactive) evaluation of different hyperparameters.
 
@@ -60,7 +60,7 @@ def cell_segmentation_with_tiling():
         predictor, image, save_path=embedding_path, tile_shape=(1024, 1024), halo=(256, 256)
     )
 
-    # Use the instance segmentation logic of SegmentAnything.
+    # Use the instance segmentation logic of Segment Anything.
     # This works by covering the image with a grid of points, getting the masks for all the poitns
     # and only keeping the plausible ones (according to the model predictions).
     # The functionality here is similar to the instance segmentation in Segment Anything,

micro_sam/evaluation/inference.py

@@ -138,7 +138,7 @@ def precompute_all_embeddings(
     To enable running different inference tasks in parallel afterwards.
 
     Args:
-        predictor: The SegmentAnything predictor.
+        predictor: The Segment Anything predictor.
         image_paths: The image file paths.
         embedding_dir: The directory where the embeddings will be saved.
     """
@@ -267,7 +267,7 @@ def run_inference_with_prompts(
     """Run segment anything inference for multiple images using prompts derived from groundtruth.
 
     Args:
-        predictor: The SegmentAnything predictor.
+        predictor: The Segment Anything predictor.
         image_paths: The image file paths.
         gt_paths: The ground-truth segmentation file paths.
         embedding_dir: The directory where the image embddings will be saved or are already saved.

micro_sam/precompute_state.py

@@ -241,7 +241,7 @@ def precompute_state(
         output_path: The output path where the embeddings and other state will be saved.
         pattern: Glob pattern to select files in a folder. The embeddings will be computed
             for each of these files. To select all files in a folder pass "*".
-        model_type: The SegmentAnything model to use. Will use the standard vit_l model by default.
+        model_type: The Segment Anything model to use. Will use the `vit_b_lm` model by default.
         checkpoint_path: Path to a checkpoint for a custom model.
         key: The key to the input file. This is needed for contaner files (e.g. hdf5 or zarr)
             or to load several images as 3d volume. Provide a glob pattern, e.g. "*.tif", for this case.

micro_sam/sam_annotator/annotator_tracking.py

@@ -213,7 +213,7 @@ def annotator_tracking(
             If `None` then the whole image is passed to Segment Anything.
         halo: Shape of the overlap between tiles, which is needed to segment objects on tile boarders.
         return_viewer: Whether to return the napari viewer to further modify it before starting the tool.
-        viewer: The viewer to which the SegmentAnything functionality should be added.
+        viewer: The viewer to which the Segment Anything functionality should be added.
             This enables using a pre-initialized viewer.
         precompute_amg_state: Whether to precompute the state for automatic mask generation.
             This will take more time when precomputing embeddings, but will then make

micro_sam/sam_annotator/image_series_annotator.py

@@ -154,7 +154,7 @@ def image_series_annotator(
         tile_shape: Shape of tiles for tiled embedding prediction.
             If `None` then the whole image is passed to Segment Anything.
         halo: Shape of the overlap between tiles, which is needed to segment objects on tile boarders.
-        viewer: The viewer to which the SegmentAnything functionality should be added.
+        viewer: The viewer to which the Segment Anything functionality should be added.
             This enables using a pre-initialized viewer.
         return_viewer: Whether to return the napari viewer to further modify it before starting the tool.
         precompute_amg_state: Whether to precompute the state for automatic mask generation.
@@ -339,7 +339,7 @@ def image_folder_annotator(
         output_folder: The folder where the segmentation results are saved.
         pattern: The glob patter for loading files from `input_folder`.
             By default all files will be loaded.
-        viewer: The viewer to which the SegmentAnything functionality should be added.
+        viewer: The viewer to which the Segment Anything functionality should be added.
             This enables using a pre-initialized viewer.
         return_viewer: Whether to return the napari viewer to further modify it before starting the tool.
         kwargs: The keyword arguments for `micro_sam.sam_annotator.image_series_annotator`.

micro_sam/training/trainable_sam.py

@@ -10,10 +10,10 @@
 
 # simple wrapper around SAM in order to keep things trainable
 class TrainableSAM(nn.Module):
-    """Wrapper to make the SegmentAnything model trainable.
+    """Wrapper to make the Segment Anything model trainable.
 
     Args:
-        sam: The SegmentAnything Model.
+        sam: The Segment Anything Model.
     """
 
     def __init__(self, sam: Sam) -> None:

micro_sam/training/util.py

@@ -150,7 +150,7 @@ def get_trainable_sam_model(
 
 
 class ConvertToSamInputs:
-    """Convert outputs of data loader to the expected batched inputs of the SegmentAnything model.
+    """Convert outputs of data loader to the expected batched inputs of the Segment Anything model.
 
     Args:
         transform: The transformation to resize the prompts. Should be the same transform used in the