Fix Cellpose v3 compatibility: update API calls and enforce v3 requirement

- Fix segmentation_instances_cyto3.py to use CellposeModel class
- Update model.eval() to unpack 3 return values instead of 4
- Enforce Cellpose v3 (>=3.0.0, <4.0.0) in install_dependencies.py
- Add note to README about using faster Cellpose v3

Author: macromeer

README.md

@@ -119,6 +119,8 @@ To start the text-based user interface in your terminal, change directory to the
 python ./scripts/user_welcome.py
 ```
 
+**Note:** T-MIDAS terminal uses the faster Cellpose v3 for optimal segmentation performance.
+
 ## Acknowledgements and Citations
 
 This project relies on several open-source libraries and tools. We would like to acknowledge and thank the creators of these projects:

scripts/install_dependencies.py

@@ -132,6 +132,6 @@ def get_mamba_version(mamba_executable):
 run_command(cmd_prefix + "python -m pip install napari[all]")
 
 print("Installing cellpose...")
-run_command(cmd_prefix + "python -m pip install cellpose")
+run_command(cmd_prefix + "python -m pip install 'cellpose>=3.0.0,<4.0.0'")
 
 print("All dependencies installed successfully.")

scripts/segmentation_instances_cyto3.py

@@ -46,13 +46,13 @@ def process_image(input_file, input_folder, model, channels, diameter, flow_thre
         labeled_time_points = np.zeros(img.shape, dtype=np.uint32)
         for t in tqdm(range(img.shape[0]), desc="Processing time points"):
             img_t = img[t]
-            mask, _, _, _ = model.eval(img_t, diameter=diameter, flow_threshold=flow_threshold, 
-                                       channels=channels, niter=2000, z_axis=0 if is_3d else None, do_3D=is_3d)
+            mask, _, _ = model.eval(img_t, diameter=diameter, flow_threshold=flow_threshold, 
+                                    channels=channels, niter=2000, z_axis=0 if is_3d else None, do_3D=is_3d)
             labeled_time_points[t] = mask
         result = labeled_time_points
     else:
-        result, _, _, _ = model.eval(img, diameter=diameter, flow_threshold=flow_threshold, 
-                                     channels=channels, niter=2000, z_axis=0 if is_3d else None, do_3D=is_3d)
+        result, _, _ = model.eval(img, diameter=diameter, flow_threshold=flow_threshold, 
+                                  channels=channels, niter=2000, z_axis=0 if is_3d else None, do_3D=is_3d)
 
     output_file = os.path.join(input_folder, input_file.replace(".tif", "_labels.tif"))
     imwrite(output_file, result.astype(np.uint32), compression='zlib')
@@ -77,7 +77,7 @@ def main():
     dim_order = args.dim_order
 
     flow_threshold = 0.4
-    model = models.Cellpose(gpu=use_GPU, model_type=args.model_type)
+    model = models.CellposeModel(gpu=use_GPU, model_type=args.model_type)
 
     input_files = [f for f in os.listdir(input_folder) if f.endswith('.tif') and not f.endswith('_labels.tif')]