Update README.md

README.md

@@ -9,6 +9,7 @@ tags:
 
 # Cellpose Nuclei Segmentation Model Trained With High Grade Serous Ovarian Cancer Dataset!
 
+
 # Dataset classes
 
 ```
@@ -21,34 +22,23 @@ nuclei_classes = {
     5: "macrophage_cytoplasm",
     6: "macrophage_nucleus",
 }
-
-tissue_classes = {
-    0: "background",
-    1: "stroma",
-    2: "omentum",
-    3: "tumor",
-    4: "hemorragia",
-    5: "necrosis",
-    6: "serum",
-}
 ```
 
-# Usage
 
-## Install cellseg_models.pytorch and albumentations
+## 1. Install cellseg_models.pytorch and albumentations
 ```
 pip install cellseg-models-pytorch
 pip install albumentations
 ```
 
-## Load trained model
+## 2. Load trained model
 ```python
 from cellseg_models_pytorch.models.cellpose import CellPose
 
 model = CellPose.from_pretrained("csmp-hub/cellpose-histo-hgsc-nuc-v1")
 ```
 
-## Run inference for one image
+## 3. Run inference for one image
 ```python
 from albumentations import Resize, Compose
 from cellseg_models_pytorch.utils import FileHandler
@@ -67,7 +57,7 @@ out = model.post_process(prob)
 # out = {"nuc": [(nuc instances (H, W), nuc types (H, W))], "cyto": None, "tissue": None}
 ```
 
-## Run inference for image batch
+## 3.1 Run inference for image batch
 ```python
 from cellseg_models_pytorch.utils import FileHandler
 
@@ -92,7 +82,7 @@ out = model.post_process(prob)
 #}
 ```
 
-## Visualize output
+## 4. Visualize output
 ```python
 from matplotlib import pyplot as plt
 from skimage.color import label2rgb
@@ -105,6 +95,8 @@ ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
 
 ## Citation
 
+cellseg_models.pytorch:
+```
 @misc{csmp2022,
     title={{cellseg_models.pytorch}: Cell/Nuclei Segmentation Models and Benchmark.},
     author={Oskari Lehtonen},
@@ -112,7 +104,10 @@ ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
     doi = {10.5281/zenodo.7064617}
     year={2022}
 }
+```
 
+Cellpose original paper:
+```
 @article{Stringer2020,
   title = {Cellpose: a generalist algorithm for cellular segmentation},
   volume = {18},
@@ -127,3 +122,4 @@ ax[2].imshow(label2rgb(out["nuc"][0][1], bg_label=0)) # type_map
   month = dec,
   pages = {100–106}
 }
+```