@@ -29,13 +29,16 @@ python convert_nnunet_ckpts.py --dataset_name_or_id DATASET_ID --MAP_root OUTPUT
 
 The RESULTS_PATH should have "inference_information.json" file created by nnunetv2 automatically, as the conversion relies on this to figure out the best model configuration and convert those for the MAP.
 
+`MAP_root` must resolve to a directory inside the current working directory. Relative paths such as `.` and `output` are supported; paths that escape the current directory are rejected.
+
 ### Command-line Arguments
 
 | Argument | Description | Required | Default |
 |----------|-------------|----------|---------|
 | `--dataset_name_or_id` | Name or ID of the nnUNet dataset to convert | Yes | N/A |
 | `--MAP_root` | Output directory for the converted MONAI bundle | No | Current directory |
 | `--nnUNet_results` | Path to nnUNet results directory with trained models | Yes | Uses environment variable if set |
+| `--checkpoint_type` | Checkpoints to convert: `final` produces `final_model.pt`, `best` produces `best_model.pt`, and `both` produces both files | No | `final` |
 
 #### Example
 
@@ -62,7 +65,7 @@ MAP_root/
     ├── 3d_fullres/           # Model configuration (if present)
     │   ├── nnunet_checkpoint.pth
     │   └── fold_X/           # Each fold's model weights
-    │       └── best_model.pt
+    │       └── final_model.pt
     ├── 3d_lowres/            # Model configuration (if present)
     └── 3d_cascade_fullres/   # Model configuration (if present)
 ```
 
@@ -17,25 +17,51 @@
 import argparse
 import os
 import sys
+from pathlib import Path
 
 # Add the current directory to the path to find the local module
 current_dir = os.path.dirname(os.path.abspath(__file__))
 if current_dir not in sys.path:
     sys.path.insert(0, current_dir)
 
-# Try importing from local apps.nnunet_bundle instead of from MONAI
-try:
-    from my_app.nnunet_bundle import convert_best_nnunet_to_monai_bundle
-except ImportError:
-    # If local import fails, try to find the module in alternate locations
+
+def _import_converter():
+    """Deferred import so nnunetv2 is loaded AFTER nnUNet_results env var is set,
+    preventing nnunetv2.paths from caching a None value for nnUNet_results."""
+    try:
+        from my_app.nnunet_bundle import convert_best_nnunet_to_monai_bundle
+
+        return convert_best_nnunet_to_monai_bundle
+    except ImportError:
+        pass
     try:
         from monai.apps.nnunet_bundle import convert_best_nnunet_to_monai_bundle
+
+        return convert_best_nnunet_to_monai_bundle
     except ImportError:
-        print(
-            "Error: Could not import convert_best_nnunet_to_monai_bundle from my_app.nnunet_bundle or apps.nnunet_bundle"
-        )
-        print("Please ensure that nnunet_bundle.py is properly installed in your project.")
-        sys.exit(1)
+        pass
+    print("Error: Could not import convert_best_nnunet_to_monai_bundle from my_app.nnunet_bundle or apps.nnunet_bundle")
+    print("Please ensure that nnunet_bundle.py is properly installed in your project.")
+    sys.exit(1)
+
+
+def _validated_map_root(value: str) -> Path:
+    """Resolve the MAP output path and keep it within the current directory."""
+    allowed_root = Path.cwd().resolve()
+    map_root = Path(value).expanduser()
+    if not map_root.is_absolute():
+        map_root = allowed_root / map_root
+    map_root = map_root.resolve(strict=False)
+
+    try:
+        map_root.relative_to(allowed_root)
+    except ValueError as exc:
+        raise argparse.ArgumentTypeError(f"MAP_root must be inside the current directory ({allowed_root}).") from exc
+
+    if map_root.exists() and not map_root.is_dir():
+        raise argparse.ArgumentTypeError(f"MAP_root is not a directory: {map_root}")
+
+    return map_root
 
 
 def parse_args():
@@ -57,6 +83,15 @@ def parse_args():
         default=None,
         help="Path to nnUNet results directory with trained models.",
     )
+    parser.add_argument(
+        "--checkpoint_type",
+        type=str,
+        default="final",
+        choices=["final", "best", "both"],
+        help="Which nnUNet checkpoint(s) to convert: 'final' (default) saves checkpoint_final.pth weights as "
+        "final_model.pt; 'best' saves checkpoint_best.pth weights as best_model.pt; "
+        "'both' saves checkpoint_final.pth as final_model.pt and checkpoint_best.pth as best_model.pt.",
+    )
     return parser.parse_args()
 
 
@@ -69,7 +104,7 @@ def main():
     }
 
     # Create the MAP root directory
-    map_root = args.MAP_root
+    map_root = _validated_map_root(args.MAP_root)
     os.makedirs(map_root, exist_ok=True)
 
     # Set nnUNet environment variables if provided
@@ -90,9 +125,12 @@ def main():
     print(f"MAP will be created at: {map_root}")
     print(f"  nnUNet_results: {os.environ.get('nnUNet_results')}")
 
+    # Import AFTER env vars are set so nnunetv2.paths caches the correct nnUNet_results value
+    convert_best_nnunet_to_monai_bundle = _import_converter()
+
     # Convert the nnUNet checkpoints to MONAI bundle format
     try:
-        convert_best_nnunet_to_monai_bundle(nnunet_config, map_root)
+        convert_best_nnunet_to_monai_bundle(nnunet_config, map_root, checkpoint_type=args.checkpoint_type)
         print(f"Successfully converted nnUNet checkpoints to MONAI bundle at: {map_root}/models")
     except Exception as e:
         print(f"Error converting nnUNet checkpoints: {e}")