Dev

.gitignore

@@ -164,6 +164,7 @@ patch_config.json
 # Patch-workflow run byproducts (regenerated by the notebooks; e.g. research/*)
 preprocessed/
 cv_results_*/
+inference_patch_config.json
 
 # Claude - share skills/settings, keep local config private
 .claude/settings.local.json

fastMONAI/_modidx.py

@@ -633,6 +633,8 @@
             'fastMONAI.vision_plot': { 'fastMONAI.vision_plot._get_slice': ('vision_plot.html#_get_slice', 'fastMONAI/vision_plot.py'),
                                        'fastMONAI.vision_plot.find_max_slice': ( 'vision_plot.html#find_max_slice',
                                                                                  'fastMONAI/vision_plot.py'),
+                                       'fastMONAI.vision_plot.show_mask_overlay': ( 'vision_plot.html#show_mask_overlay',
+                                                                                    'fastMONAI/vision_plot.py'),
                                        'fastMONAI.vision_plot.show_med_img': ('vision_plot.html#show_med_img', 'fastMONAI/vision_plot.py'),
                                        'fastMONAI.vision_plot.show_segmentation_comparison': ( 'vision_plot.html#show_segmentation_comparison',
                                                                                                'fastMONAI/vision_plot.py'),

fastMONAI/vision_all.py

@@ -1,4 +1,5 @@
 #imports all of the functions and classes from the fastMONAI library
+from .vision_plot import show_segmentation_comparison
 from .vision_core import *
 from .vision_data import *
 from .vision_augmentation import *

fastMONAI/vision_plot.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/00_vision_plot.ipynb.
 
 # %% auto #0
-__all__ = ['validate_anatomical_plane', 'show_med_img', 'find_max_slice', 'show_segmentation_comparison']
+__all__ = ['validate_anatomical_plane', 'show_med_img', 'find_max_slice', 'show_segmentation_comparison', 'show_mask_overlay']
 
 # %% ../nbs/00_vision_plot.ipynb #7ea97ee7-8a55-485d-a75c-ae1a8055f489
 import warnings
@@ -196,3 +196,57 @@ def show_segmentation_comparison(
     axes[2].axis('off')
 
     plt.tight_layout()
+
+# %% ../nbs/00_vision_plot.ipynb #72d68ebd
+def show_mask_overlay(image, mask, ctx=None, channel: int = 0, slice_index: int = None,
+                      anatomical_plane: int = 2, voxel_size=None, alpha: float = 0.5,
+                      cmap_img: str = 'gray', cmap_mask: str = 'autumn',
+                      ax=None, figsize=None, title=None, **kwargs):
+    """Overlay `mask` on `image` on a single axis, using the same slicing and orientation as `.show()`.
+
+    Draws the image, then the mask on top with zero voxels transparent, so it lines up with
+    `MedImage.show()` / `MedMask.show()`. Reuses the same `_get_slice` those methods use.
+
+    Args:
+        image: Background image (MedImage or object with `.data` [C, H, W, D]).
+        mask: Overlay mask (MedMask or object with `.data` [C, H, W, D]); zero voxels are transparent.
+        ctx: Axis to draw on (fastai-style context); same role as `ax`.
+        channel: Channel to display.
+        slice_index: Slice to show; if None, uses `find_max_slice(mask, anatomical_plane)`.
+        anatomical_plane: 0=sagittal, 1=coronal, 2=axial (default).
+        voxel_size: Voxel spacing for aspect ratio; if None, aspect defaults to 1.
+        alpha: Overlay opacity.
+        cmap_img: Colormap for the background image.
+        cmap_mask: Colormap for the mask overlay.
+        ax: Axis to draw on (overrides `ctx`).
+        figsize: Figure size when a new axis is created.
+        title: Optional axis title.
+        kwargs: Extra args forwarded to the background image `imshow`.
+
+    Returns:
+        The matplotlib axis with the overlay.
+    """
+    validate_anatomical_plane(anatomical_plane)
+
+    img_data = image.data if hasattr(image, 'data') else image
+    mask_data = mask.data if hasattr(mask, 'data') else mask
+    if hasattr(img_data, 'cpu'): img_data = img_data.cpu()
+    if hasattr(mask_data, 'cpu'): mask_data = mask_data.cpu()
+
+    if slice_index is None:
+        slice_index = find_max_slice(mask_data[channel].numpy(), anatomical_plane)
+
+    img_slice, aspect = _get_slice(img_data, channel, slice_index, anatomical_plane, voxel_size)
+    mask_slice, _ = _get_slice(mask_data, channel, slice_index, anatomical_plane, voxel_size)
+
+    ax = ax if ax is not None else ctx
+    if ax is None:
+        _, ax = plt.subplots(figsize=figsize)
+
+    ax.imshow(img_slice, cmap=cmap_img, aspect=aspect, **kwargs)
+    ax.imshow(np.ma.masked_where(mask_slice == 0, mask_slice), cmap=cmap_mask, alpha=alpha, aspect=aspect)
+
+    if title is not None:
+        ax.set_title(title)
+    ax.axis('off')
+    return ax

nbs/00_vision_plot.ipynb

@@ -174,6 +174,14 @@
    "metadata": {},
    "outputs": [],
    "source": "#| export\ndef show_segmentation_comparison(\n    image, ground_truth, prediction,\n    slice_index: int = None,\n    anatomical_plane: int = 2,\n    metric_value: float = None,\n    metric_name: str = 'DSC',\n    channel: int = 0,\n    figsize: tuple = (15, 5),\n    cmap_img: str = 'gray',\n    cmap_mask: str = 'gray',\n    voxel_size = None\n):\n    \"\"\"Display 3-panel comparison: Image | Ground Truth | Prediction.\n    \n    Useful for validating segmentation results, especially after patch-based \n    inference where results are not in standard fastai batch format.\n    \n    Args:\n        image: Input image (MedImage, MedMask, or tensor [C, H, W, D])\n        ground_truth: Ground truth mask (MedMask or tensor [C, H, W, D])\n        prediction: Predicted mask (tensor [C, H, W, D])\n        slice_index: Slice to display. If None, uses find_max_slice on ground_truth.\n        anatomical_plane: 0=sagittal, 1=coronal, 2=axial (default)\n        metric_value: Optional metric value to display in prediction title\n        metric_name: Name of metric for title (default 'DSC')\n        channel: Channel to display for multi-channel data (default 0)\n        figsize: Figure size (default (15, 5))\n        cmap_img: Colormap for image (default 'gray')\n        cmap_mask: Colormap for masks (default 'gray')\n        voxel_size: Voxel spacing for aspect ratio. If None, aspect=1.\n\n    Example::\n    \n        # After patch_inference()\n        show_segmentation_comparison(\n            image=val_img,\n            ground_truth=val_gt,\n            prediction=predictions[0],\n            metric_value=results_df.iloc[0]['DSC'],\n            anatomical_plane=2\n        )\n    \"\"\"\n    validate_anatomical_plane(anatomical_plane)\n    \n    img_data = image.data if hasattr(image, 'data') else image\n    gt_data = ground_truth.data if hasattr(ground_truth, 'data') else ground_truth\n    pred_data = prediction.data if hasattr(prediction, 'data') else prediction\n    \n    if hasattr(img_data, 'cpu'): img_data = img_data.cpu()\n    if hasattr(gt_data, 'cpu'): gt_data = gt_data.cpu()\n    if hasattr(pred_data, 'cpu'): pred_data = pred_data.cpu()\n    \n    if slice_index is None:\n        gt_np = gt_data[channel].numpy()\n        slice_index = find_max_slice(gt_np, anatomical_plane)\n    \n    fig, axes = plt.subplots(1, 3, figsize=figsize)\n    \n    img_slice, img_aspect = _get_slice(img_data, channel, slice_index, anatomical_plane, voxel_size)\n    gt_slice, gt_aspect = _get_slice(gt_data, channel, slice_index, anatomical_plane, voxel_size)\n    pred_slice, pred_aspect = _get_slice(pred_data, channel, slice_index, anatomical_plane, voxel_size)\n    \n    axes[0].imshow(img_slice, cmap=cmap_img, aspect=img_aspect)\n    axes[0].set_title('Input Image')\n    axes[0].axis('off')\n    \n    axes[1].imshow(gt_slice, cmap=cmap_mask, aspect=gt_aspect)\n    axes[1].set_title('Ground Truth')\n    axes[1].axis('off')\n    \n    pred_title = 'Prediction'\n    if metric_value is not None:\n        pred_title = f'Prediction ({metric_name}: {metric_value:.4f})'\n    \n    axes[2].imshow(pred_slice, cmap=cmap_mask, aspect=pred_aspect)\n    axes[2].set_title(pred_title)\n    axes[2].axis('off')\n    \n    plt.tight_layout()"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72d68ebd",
+   "metadata": {},
+   "outputs": [],
+   "source": "#| export\ndef show_mask_overlay(image, mask, ctx=None, channel: int = 0, slice_index: int = None,\n                      anatomical_plane: int = 2, voxel_size=None, alpha: float = 0.5,\n                      cmap_img: str = 'gray', cmap_mask: str = 'autumn',\n                      ax=None, figsize=None, title=None, **kwargs):\n    \"\"\"Overlay `mask` on `image` on a single axis, using the same slicing and orientation as `.show()`.\n\n    Draws the image, then the mask on top with zero voxels transparent, so it lines up with\n    `MedImage.show()` / `MedMask.show()`. Reuses the same `_get_slice` those methods use.\n\n    Args:\n        image: Background image (MedImage or object with `.data` [C, H, W, D]).\n        mask: Overlay mask (MedMask or object with `.data` [C, H, W, D]); zero voxels are transparent.\n        ctx: Axis to draw on (fastai-style context); same role as `ax`.\n        channel: Channel to display.\n        slice_index: Slice to show; if None, uses `find_max_slice(mask, anatomical_plane)`.\n        anatomical_plane: 0=sagittal, 1=coronal, 2=axial (default).\n        voxel_size: Voxel spacing for aspect ratio; if None, aspect defaults to 1.\n        alpha: Overlay opacity.\n        cmap_img: Colormap for the background image.\n        cmap_mask: Colormap for the mask overlay.\n        ax: Axis to draw on (overrides `ctx`).\n        figsize: Figure size when a new axis is created.\n        title: Optional axis title.\n        kwargs: Extra args forwarded to the background image `imshow`.\n\n    Returns:\n        The matplotlib axis with the overlay.\n    \"\"\"\n    validate_anatomical_plane(anatomical_plane)\n\n    img_data = image.data if hasattr(image, 'data') else image\n    mask_data = mask.data if hasattr(mask, 'data') else mask\n    if hasattr(img_data, 'cpu'): img_data = img_data.cpu()\n    if hasattr(mask_data, 'cpu'): mask_data = mask_data.cpu()\n\n    if slice_index is None:\n        slice_index = find_max_slice(mask_data[channel].numpy(), anatomical_plane)\n\n    img_slice, aspect = _get_slice(img_data, channel, slice_index, anatomical_plane, voxel_size)\n    mask_slice, _ = _get_slice(mask_data, channel, slice_index, anatomical_plane, voxel_size)\n\n    ax = ax if ax is not None else ctx\n    if ax is None:\n        _, ax = plt.subplots(figsize=figsize)\n\n    ax.imshow(img_slice, cmap=cmap_img, aspect=aspect, **kwargs)\n    ax.imshow(np.ma.masked_where(mask_slice == 0, mask_slice), cmap=cmap_mask, alpha=alpha, aspect=aspect)\n\n    if title is not None:\n        ax.set_title(title)\n    ax.axis('off')\n    return ax"
   }
  ],
  "metadata": {