From 5c426d0cc1bd3328116dc96eac7afbf2a98c0b98 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 16 Jan 2025 10:29:40 -0800
Subject: [PATCH 01/20] Update tool.py

---
 ImageTool/tool.py | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 4242572..03fe246 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -280,3 +280,42 @@ def sitk2ant(img, reverse = False):
         delete_if_exist("tmp.nii")
         return dcm
     
+def plot3d(CFR_crop, vmax = 3.5, sample_rate = 5):
+    matplotlib.use('module://ipympl.backend_nbagg')
+    fig = plt.figure(figsize=(8, 8))
+    ax = fig.add_subplot(111, projection="3d")
+
+    x, y, z = np.indices(CFR_crop.shape)
+
+    # Filter the coordinates and CFR values using the mask
+    mask_indices = CFR_crop[:].nonzero()
+    x_masked = x[mask_indices]
+    y_masked = y[mask_indices]
+    z_masked = z[mask_indices]
+    CFR_masked = CFR_crop[mask_indices]
+
+    x_masked = x_masked[::sample_rate]
+    y_masked = y_masked[::sample_rate]
+    z_masked = z_masked[::sample_rate]
+    CFR_masked = CFR_masked[::sample_rate]
+
+    ax.set_xlabel("X-axis")
+    ax.set_ylabel("Y-axis")
+    ax.set_zlabel("Z-axis")
+    ax.set_title("Interactive 3D CFR Visualization")
+    # Plot only the masked values
+    scatter = ax.scatter(
+        x_masked,
+        y_masked,
+        z_masked,
+        c=CFR_masked,
+        cmap="jet",
+        vmin=0,
+        vmax=vmax,
+        s=1
+    )
+
+    # Add a colorbar and adjust its position
+    colorbar = fig.colorbar(scatter, ax=ax, shrink=0.6, aspect=15, pad=0.1)
+    colorbar.set_label("CFR Intensity")
+    plt.show()

From f1cd8a6f738a11354512bdaef34b1f28abde2f95 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 16 Jan 2025 10:30:06 -0800
Subject: [PATCH 02/20] Update tool.py

---
 ImageTool/tool.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 03fe246..6297d5a 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -7,6 +7,7 @@
 import matplotlib.pyplot as plt
 from scipy import ndimage
 import cv2
+import matplotlib
 import os 
 import shutil
 from skimage.metrics import structural_similarity 

From dad481e617463110f6ab8dd7bca952f5f58b354d Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Mon, 27 Jan 2025 11:12:20 -0800
Subject: [PATCH 03/20] Update tool.py

---
 ImageTool/tool.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 6297d5a..264f938 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -12,6 +12,20 @@
 import shutil
 from skimage.metrics import structural_similarity 
 import ants
+import monai
+def get_HU_error(y_pred, y, percentile = None, onehot = False, n_classes = None):
+    def get_onehot(label, n_classes):
+        one_hot = np.eye(n_classes)[label]  # Shape: (1, 32, 32, 32, 6)
+        one_hot = one_hot.transpose(3, 0, 1, 2)
+        one_hot = np.expand_dims(one_hot, axis=0)
+
+        return one_hot
+        
+    if onehot is False:
+        y = get_onehot(y, n_classes)  # Shape: (1, 32, 32, 32, 6)
+        y_pred = get_onehot(y_pred, n_classes) 
+    return monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=percentile, directed=False, spacing=None)
+    
 def get_contour(binary_mask):
 
     # Multiply by 255 to convert it to the format expected by OpenCV (0s and 255s)

From 112b64ea40e1ce579220024bcf4ffc39ca5c461f Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Mon, 27 Jan 2025 11:14:38 -0800
Subject: [PATCH 04/20] Update setup.py

---
 setup.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index c22449c..21409c0 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,9 @@
       "matplotlib",
       "numpy",
       "opencv-python",
-      "scikit-image"
+      "scikit-image",
+      "antspyx",
+      "monai"
     ],
     classifiers=[              # Metadata
         "Programming Language :: Python :: 3",

From 30b647a3d06fbbac448d4f5c5ed9dad05afaf5e6 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Tue, 4 Feb 2025 16:06:18 -0800
Subject: [PATCH 05/20] Update tool.py

---
 ImageTool/tool.py | 171 ++++++++++++++++++++++++----------------------
 1 file changed, 91 insertions(+), 80 deletions(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 264f938..cb704d0 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -5,15 +5,25 @@
 from ipywidgets import interact
 from ipywidgets.widgets import IntSlider
 import matplotlib.pyplot as plt
-from scipy import ndimage
-import cv2
 import matplotlib
+import cv2
 import os 
 import shutil
+from totalsegmentator.python_api import totalsegmentator
 from skimage.metrics import structural_similarity 
+from scipy import ndimage
 import ants
 import monai
-def get_HU_error(y_pred, y, percentile = None, onehot = False, n_classes = None):
+import monai.metrics
+
+def compute_hu_distance():
+    for i in range(1, n_classes):
+        data.append(monai.metrics.compute_hausdorff_distance((y_pred == i), (y == i), include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None))
+    return data
+    
+    
+
+def get_HU_error(y_pred, y, onehot = False, n_classes = None):
     def get_onehot(label, n_classes):
         one_hot = np.eye(n_classes)[label]  # Shape: (1, 32, 32, 32, 6)
         one_hot = one_hot.transpose(3, 0, 1, 2)
@@ -24,7 +34,68 @@ def get_onehot(label, n_classes):
     if onehot is False:
         y = get_onehot(y, n_classes)  # Shape: (1, 32, 32, 32, 6)
         y_pred = get_onehot(y_pred, n_classes) 
-    return monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=percentile, directed=False, spacing=None)
+    return monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None)    
+    
+    
+def erode3d(mask=np.ones((6, 6, 6)), size=2):
+    # Ensure mask is boolean
+    mask = mask[:]
+    
+    # Create a 3D structuring element
+    structure = np.ones((2 * size + 1, 2 * size + 1, 2 * size + 1), dtype=bool)
+    
+    # Perform binary erosion
+    eroded_mask = ndimage.binary_erosion(mask, structure=structure).astype(mask.dtype)
+    
+    return eroded_mask
+
+def plot3d(CFR_crop, mask = None, vmax = 2, sample_rate = 5):
+    matplotlib.use('module://ipympl.backend_nbagg')
+    fig = plt.figure(figsize=(8, 8))
+    ax = fig.add_subplot(111, projection="3d")
+
+    x, y, z = np.indices(CFR_crop.shape)
+
+    # Filter the coordinates and CFR values using the mask
+    if mask:
+        mask = mask[:]
+        mask = mask - erode3d(mask, size = 1)
+        mask = mask.astype(bool)
+        mask_indices = np.where(mask)
+
+    else:
+        mask_indices = CFR_crop[:].nonzero()
+    x_masked = x[mask_indices]
+    y_masked = y[mask_indices]
+    z_masked = z[mask_indices]
+    CFR_masked = CFR_crop[mask_indices]
+
+    x_masked = x_masked[::sample_rate]
+    y_masked = y_masked[::sample_rate]
+    z_masked = z_masked[::sample_rate]
+    CFR_masked = CFR_masked[::sample_rate]
+
+    ax.set_xlabel("X-axis")
+    ax.set_ylabel("Y-axis")
+    ax.set_zlabel("Z-axis")
+    ax.set_title("Interactive 3D CFR Visualization")
+    # Plot only the masked values
+    scatter = ax.scatter(
+        x_masked,
+        y_masked,
+        z_masked,
+        c=CFR_masked,
+        cmap="jet",
+        vmin=0,
+        vmax=vmax,
+        s=1
+    )
+
+    # Add a colorbar and adjust its position
+    colorbar = fig.colorbar(scatter, ax=ax, shrink=0.6, aspect=15, pad=0.1)
+    colorbar.set_label("CFR Intensity")
+    plt.show()
+    
     
 def get_contour(binary_mask):
 
@@ -35,21 +106,6 @@ def get_contour(binary_mask):
     cv2.drawContours(contour_only_mask, contours, -1, 1, 1)  
     return contour_only_mask
 
-def erode(mask = np.ones((6, 6)), size = 2):
-    structure = np.ones((2*size + 1, 2*size + 1))
-    # Erode the mask
-    eroded_mask = ndimage.binary_erosion(mask, structure).astype(mask.dtype)
-    return eroded_mask
-
-def calculate_mean_hu(dcm_rest, dcm_mask_rest, bolus_rest_init):
-    idxes =  [i for i in range(dcm_rest.shape[2]) if np.sum(dcm_mask_rest[:, :, i]) > 100]
-    slice_idx = max([(tool.ssim(dcm_rest[:,:,i], bolus_rest_init), i) for i in idxes])[1]
-    print(slice_idx)
-    reg_ss_rest = ants.registration(fixed = ants.from_numpy(dcm_rest[:, :, slice_idx]) , moving = ants.from_numpy(bolus_rest_init), type_of_transform ='SyNAggro')['warpedmovout']
-    print(slice_idx, np.sum(dcm_mask_rest[:, :, slice_idx]))
-    mask = erode(dcm_mask_rest[:, :, slice_idx], size = 2).astype(bool)
-    HD_rest = np.mean(reg_ss_rest[:][mask])
-    return HD_rest
 
 def resize(image_path, target_size, output_path = None):
     image = sitk.ReadImage(image_path)
@@ -132,12 +188,12 @@ def plot_mask(mask_for_cv, ax, color = "Blues", alpha = 0.5, label = None):
     return masked_mask
 
 
-def list_display(img_list, name = "", read_dcm = False):
+def list_display(img_list, name = "", vmax = 300, cmap = 'jet', read_dcm = False):
 
     
     if read_dcm:
         img_list = [sitk.GetArrayFromImage(sitk.ReadImage(dcm_file)) for dcm_file in img_list]
-    max_slices_contrast = max(img_list, key = lambda x: x.shape[0]).shape[0]
+    max_slices_contrast = max(img_list, key = lambda x: x.shape[2]).shape[2]
 
     contrast_slice_slider = widgets.IntSlider(min=0, max=max_slices_contrast-1, step=1, value=0, description='Slice:')
     def display_slice(contrast_slice_index, img_list, name):
@@ -146,11 +202,11 @@ def display_slice(contrast_slice_index, img_list, name):
         fig.suptitle(name + f'  Slice {contrast_slice_index}')
         for i, img in enumerate(img_list):
             if img is not None:
-                axes[i].imshow(img[min(img.shape[0] - 1, contrast_slice_index),:,:], cmap='jet', vmax = 300)
+                axes[i].imshow(img[:,:,min(img.shape[2] - 1, contrast_slice_index)], cmap=cmap, vmin = 0, vmax = vmax)
                 axes[i].axis('off')
         plt.show()
+        
     def update(contrast_slice_index):
-
         display_slice(contrast_slice_index, img_list, name)
 
     widgets.interact(update, contrast_slice_index=contrast_slice_slider)
@@ -158,7 +214,6 @@ def update(contrast_slice_index):
 
 def folders_display(img_list, name = "", read_dcm = False):
 
-    
     if read_dcm:
         img_list = [sitk.GetArrayFromImage(sitk.ReadImage(dcm_file)) for dcm_file in img_list]
     max_slices_contrast = max(img_list, key = lambda x: x.shape[0]).shape[0]
@@ -181,7 +236,7 @@ def display_slice(contrast_slice_index, img_list, name):
     plt.show()
 
 
-def lists_display(img_lists, name = None, titles = None):
+def lists_display(img_lists, cmap = 'jet', name = None, titles = None):
     if name == None:
         name = ""
     if titles == None:
@@ -208,7 +263,7 @@ def display_slice(contrast_slice_index, list_idx_slider):
             if imgs is not None:
                 
                 img = imgs[min(len(imgs),list_idx_slider)]
-                axes[i].imshow(img[min(img.shape[0] - 1, contrast_slice_index),:,:], cmap='jet', vmax = vmaxs[i])
+                axes[i].imshow(img[min(img.shape[0] - 1, contrast_slice_index),:,:], cmap=cmap, vmax = vmaxs[i])
                 axes[i].axis('off')
                 axes[i].set_title(titles[i])
         plt.show()
@@ -233,19 +288,7 @@ def make_if_dont_exist(folder_path,overwrite=False):
             os.makedirs(folder_path)
     else:
         os.makedirs(folder_path)
-def make_if_dont_exist(folder_path,overwrite=False):
-    """
-    creates a folder if it does not exists
-    input: 
-    folder_path : relative path of the folder which needs to be created
-    over_write :(default: False) if True overwrite the existing folder 
-    """
-    if os.path.exists(folder_path):
-        if overwrite:
-            shutil.rmtree(folder_path)
-            os.makedirs(folder_path)
-    else:
-        os.makedirs(folder_path)
+
 def load_3d_2d(dcm_file, output_path):
     make_if_dont_exist(output_path)
     # Read the 3D DICOM image using SimpleITK
@@ -276,13 +319,13 @@ def ssim(np_image1, np_image2):
     data_range = np.max([np_image1.max(), np_image2.max()]) - np.min([np_image1.min(), np_image2.min()])
     return structural_similarity(np_image1, np_image2, data_range=data_range)
     
-def delete_is_exist(file_path):
+def delete_if_exist(file_path):
     if os.path.exists(file_path):
         os.remove(file_path)
         print(f"File {file_path} has been deleted.")
     else:
         print(f"File {file_path} does not exist.")
-
+        
 def sitk2ant(img, reverse = False):
     if reverse == True:
         ants.image_write(img, "tmp.nii")
@@ -295,42 +338,10 @@ def sitk2ant(img, reverse = False):
         delete_if_exist("tmp.nii")
         return dcm
     
-def plot3d(CFR_crop, vmax = 3.5, sample_rate = 5):
-    matplotlib.use('module://ipympl.backend_nbagg')
-    fig = plt.figure(figsize=(8, 8))
-    ax = fig.add_subplot(111, projection="3d")
-
-    x, y, z = np.indices(CFR_crop.shape)
-
-    # Filter the coordinates and CFR values using the mask
-    mask_indices = CFR_crop[:].nonzero()
-    x_masked = x[mask_indices]
-    y_masked = y[mask_indices]
-    z_masked = z[mask_indices]
-    CFR_masked = CFR_crop[mask_indices]
-
-    x_masked = x_masked[::sample_rate]
-    y_masked = y_masked[::sample_rate]
-    z_masked = z_masked[::sample_rate]
-    CFR_masked = CFR_masked[::sample_rate]
-
-    ax.set_xlabel("X-axis")
-    ax.set_ylabel("Y-axis")
-    ax.set_zlabel("Z-axis")
-    ax.set_title("Interactive 3D CFR Visualization")
-    # Plot only the masked values
-    scatter = ax.scatter(
-        x_masked,
-        y_masked,
-        z_masked,
-        c=CFR_masked,
-        cmap="jet",
-        vmin=0,
-        vmax=vmax,
-        s=1
-    )
-
-    # Add a colorbar and adjust its position
-    colorbar = fig.colorbar(scatter, ax=ax, shrink=0.6, aspect=15, pad=0.1)
-    colorbar.set_label("CFR Intensity")
-    plt.show()
+def erode(mask = np.ones((6, 6)), size = 2):
+    mask = mask[:]
+    structure = np.ones((2*size + 1, 2*size + 1))
+    # Erode the mask
+    eroded_mask = ndimage.binary_erosion(mask, structure).astype(mask.dtype)
+    return eroded_mask
+	

From 5cbe80e154fa59a78b5f5bba95a4a6b6d4c01e60 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Wed, 5 Feb 2025 15:13:09 -0800
Subject: [PATCH 06/20] Update setup.py

---
 setup.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index 21409c0..38a8d01 100644
--- a/setup.py
+++ b/setup.py
@@ -9,11 +9,12 @@
       "SimpleITK",
       "ipywidgets",
       "matplotlib",
-      "numpy",
+      "numpy==1.25",
       "opencv-python",
       "scikit-image",
-      "antspyx",
-      "monai"
+      "antspyx==0.4.2",
+      "monai",
+      "scipy"
     ],
     classifiers=[              # Metadata
         "Programming Language :: Python :: 3",

From 872a04a12fe249160d4ad0a6fd7f4cc174da9a40 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 6 Feb 2025 11:11:46 -0800
Subject: [PATCH 07/20] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 38a8d01..96e0e7a 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
     install_requires=[         # List of dependencies
       "SimpleITK",
       "ipywidgets",
-      "matplotlib",
+      "matplotlib==3.5.1",
       "numpy==1.25",
       "opencv-python",
       "scikit-image",

From 083c1b130bfac0cb04530f70294e785ebf5bddc5 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 6 Feb 2025 11:35:14 -0800
Subject: [PATCH 08/20] Update setup.py

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 96e0e7a..677fc5e 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='Image-Toolbox',
+    name='ImageTool',
     version="0.1.0",
     description="A short description of your package",
     packages=find_packages(),  # Automatically find and include your package
@@ -19,5 +19,5 @@
     classifiers=[              # Metadata
         "Programming Language :: Python :: 3",
     ],
-    python_requires='>=3.8',
+    python_requires'>=3.8',
 )

From 07e272f33a6097a3a607b13874c2d308fdbd7886 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 6 Feb 2025 12:05:00 -0800
Subject: [PATCH 09/20] Update tool.py

---
 ImageTool/tool.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index cb704d0..186f30a 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -16,7 +16,8 @@
 import monai
 import monai.metrics
 
-def compute_hu_distance():
+def compute_hu_distance(n_classes, y_pred, y):
+    data = []
     for i in range(1, n_classes):
         data.append(monai.metrics.compute_hausdorff_distance((y_pred == i), (y == i), include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None))
     return data

From 1fd45a23411e5b5bb320b8232332ba6ab1dcb352 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 6 Feb 2025 12:05:54 -0800
Subject: [PATCH 10/20] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 677fc5e..ab78cd6 100644
--- a/setup.py
+++ b/setup.py
@@ -19,5 +19,5 @@
     classifiers=[              # Metadata
         "Programming Language :: Python :: 3",
     ],
-    python_requires'>=3.8',
+    python_requires='>=3.8',
 )

From d6916986937af2c353b128412b06187228750d52 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 6 Feb 2025 14:06:59 -0800
Subject: [PATCH 11/20] Update tool.py

---
 ImageTool/tool.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 186f30a..0753943 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -9,7 +9,6 @@
 import cv2
 import os 
 import shutil
-from totalsegmentator.python_api import totalsegmentator
 from skimage.metrics import structural_similarity 
 from scipy import ndimage
 import ants

From be9eaf2eca30c524f3f5e1c532c12b95614bbf7e Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Wed, 19 Feb 2025 15:37:48 -0800
Subject: [PATCH 12/20] Update tool.py

---
 ImageTool/tool.py | 47 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 0753943..e8e98db 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -14,7 +14,54 @@
 import ants
 import monai
 import monai.metrics
+import nibabel as nib
+import nibabel.processing
+def as_closest_canonical(img_in):
+    """
+    Convert the given nifti file to the closest canonical nifti file.
+    """
+    return nib.as_closest_canonical(img_in)
+
+
+def as_closest_canonical_nifti(path_in, path_out):
+    """
+    Convert the given nifti file to the closest canonical nifti file.
+    """
+    img_in = nib.load(path_in)
+    img_out = nib.as_closest_canonical(img_in)
+    nib.save(img_out, path_out)
+
+
+def undo_canonical(img_can, img_orig):
+    """
+    Inverts nib.to_closest_canonical()
+
+    img_can: the image we want to move back
+    img_orig: the original image because transforming to canonical
+
+    returns image in original space
+
+    https://github.com/nipy/nibabel/issues/1063
+    """
+    from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
+
+    img_ornt = io_orientation(img_orig.affine)
+    ras_ornt = axcodes2ornt("RAS")
+
+    to_canonical = img_ornt  # Same as ornt_transform(img_ornt, ras_ornt)
+    from_canonical = ornt_transform(ras_ornt, img_ornt)
+
+    # Same as as_closest_canonical
+    # img_canonical = img_orig.as_reoriented(to_canonical)
+
+    return img_can.as_reoriented(from_canonical)
+
 
+def undo_canonical_nifti(path_in_can, path_in_orig, path_out):
+    e = nib.load(path_in_can)
+    img_orig = nib.load(path_in_orig)
+    img_out = undo_canonical(img_can, img_orig)
+    nib.save(img_out, path_out)
 def compute_hu_distance(n_classes, y_pred, y):
     data = []
     for i in range(1, n_classes):

From d34a6ccf1d04e814e6d56856824a596029b217de Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Wed, 19 Feb 2025 15:50:03 -0800
Subject: [PATCH 13/20] Update tool.py

---
 ImageTool/tool.py | 21 +++------------------
 1 file changed, 3 insertions(+), 18 deletions(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index e8e98db..ca7774e 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -16,11 +16,7 @@
 import monai.metrics
 import nibabel as nib
 import nibabel.processing
-def as_closest_canonical(img_in):
-    """
-    Convert the given nifti file to the closest canonical nifti file.
-    """
-    return nib.as_closest_canonical(img_in)
+from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
 
 
 def as_closest_canonical_nifti(path_in, path_out):
@@ -33,18 +29,6 @@ def as_closest_canonical_nifti(path_in, path_out):
 
 
 def undo_canonical(img_can, img_orig):
-    """
-    Inverts nib.to_closest_canonical()
-
-    img_can: the image we want to move back
-    img_orig: the original image because transforming to canonical
-
-    returns image in original space
-
-    https://github.com/nipy/nibabel/issues/1063
-    """
-    from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
-
     img_ornt = io_orientation(img_orig.affine)
     ras_ornt = axcodes2ornt("RAS")
 
@@ -58,10 +42,11 @@ def undo_canonical(img_can, img_orig):
 
 
 def undo_canonical_nifti(path_in_can, path_in_orig, path_out):
-    e = nib.load(path_in_can)
+    img_can = nib.load(path_in_can)
     img_orig = nib.load(path_in_orig)
     img_out = undo_canonical(img_can, img_orig)
     nib.save(img_out, path_out)
+	
 def compute_hu_distance(n_classes, y_pred, y):
     data = []
     for i in range(1, n_classes):

From a9f82c7c81a24fa2d08101611bd9e393cc69dd50 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 20 Feb 2025 11:19:35 -0800
Subject: [PATCH 14/20] Update tool.py

---
 ImageTool/tool.py | 29 +++++++++++++++++++++++++----
 1 file changed, 25 insertions(+), 4 deletions(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index ca7774e..bfa3261 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -190,16 +190,37 @@ def crop_with_bbox(image_path, bbox, output_path = None):
         sitk.WriteImage(cropped_image, output_path)
     return cropped_image
   
-def load_2d_3d(dicom_folder, output_path = None):
-    # Use SimpleITK to read the DICOM series
+def load_2d_3d(dicom_input, output_path=None):
+    """
+    Convert a folder of DICOM files or a list of DICOM file paths into a 3D NIfTI image.
+    
+    Parameters:
+        dicom_input (str or list): Either a folder containing DICOM files or a list of file paths.
+        output_path (str, optional): Path to save the output NIfTI file.
+
+    Returns:
+        sitk.Image: The 3D image.
+    """
     reader = sitk.ImageSeriesReader()
-    # Get the list of DICOM file names from the directory
-    dicom_files = reader.GetGDCMSeriesFileNames(dicom_folder)
+
+    if isinstance(dicom_input, str) and os.path.isdir(dicom_input):
+        # If dicom_input is a folder, get the list of DICOM files
+        dicom_files = reader.GetGDCMSeriesFileNames(dicom_input)
+    elif isinstance(dicom_input, list) and all(os.path.isfile(f) for f in dicom_input):
+        # If dicom_input is a list of files, use it directly
+        dicom_files = dicom_input
+    else:
+        raise ValueError("dicom_input must be a valid folder path or a list of file paths.")
+
     # Load the DICOM series
     reader.SetFileNames(dicom_files)
     image = reader.Execute()
+
+    # Save as NIfTI if output_path is provided
     if output_path:
         sitk.WriteImage(image, output_path)
+        print(f"3D NIfTI saved at: {output_path}")
+
     return image
 
 def plot_contour(mask_for_cv, ax, color = "red", label = None):

From 07198e6a5ea9e085656587eebeb858ba648bf5b9 Mon Sep 17 00:00:00 2001
From: Qiyu Zhang <148504334+Qiyu-Zh@users.noreply.github.com>
Date: Thu, 20 Feb 2025 15:41:07 -0800
Subject: [PATCH 15/20] Update tool.py

---
 ImageTool/tool.py | 87 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index bfa3261..0e41721 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -18,6 +18,93 @@
 import nibabel.processing
 from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
 
+import SimpleITK as sitk
+
+def demons_registration(
+    fixed_image, moving_image, fixed_mask=None, moving_mask=None, fixed_points=None, moving_points=None
+):
+    registration_method = sitk.ImageRegistrationMethod()
+
+    # Create initial identity transformation.
+    transform_to_displacement_field_filter = sitk.TransformToDisplacementFieldFilter()
+    transform_to_displacement_field_filter.SetReferenceImage(fixed_image)
+    
+    initial_transform = sitk.DisplacementFieldTransform(
+        transform_to_displacement_field_filter.Execute(sitk.Transform())
+    )
+
+    # Regularization (update field - viscous, total field - elastic).
+    initial_transform.SetSmoothingGaussianOnUpdate(
+        varianceForUpdateField=0.0, varianceForTotalField=2.0
+    )
+
+    registration_method.SetInitialTransform(initial_transform)
+
+    # Set Demons metric
+    registration_method.SetMetricAsDemons(10)  # intensities are equal if diff < 10HU
+
+    # Provide masks if available
+    if fixed_mask is not None:
+        registration_method.SetMetricFixedMask(fixed_mask)
+    if moving_mask is not None:
+        registration_method.SetMetricMovingMask(moving_mask)
+
+    # Multi-resolution framework.
+    registration_method.SetShrinkFactorsPerLevel(shrinkFactors=[4, 2, 1])
+    registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[8, 4, 0])
+
+    registration_method.SetInterpolator(sitk.sitkLinear)
+    
+    # Optimizer
+    registration_method.SetOptimizerAsGradientDescent(
+        learningRate=1.0,
+        numberOfIterations=20,
+        convergenceMinimumValue=1e-6,
+        convergenceWindowSize=10,
+    )
+    registration_method.SetOptimizerScalesFromPhysicalShift()
+
+    # If corresponding points are given, display similarity metric and TRE
+    if fixed_points and moving_points:
+        registration_method.AddCommand(
+            sitk.sitkStartEvent, rc.metric_and_reference_start_plot
+        )
+        registration_method.AddCommand(
+            sitk.sitkEndEvent, rc.metric_and_reference_end_plot
+        )
+        registration_method.AddCommand(
+            sitk.sitkIterationEvent,
+            lambda: rc.metric_and_reference_plot_values(
+                registration_method, fixed_points, moving_points
+            ),
+        )
+
+    # Execute the registration
+    final_transform = registration_method.Execute(fixed_image, moving_image)
+
+    # Apply the transformation to the moving image
+    registered_moving_image = sitk.Resample(
+        moving_image,
+        fixed_image,  # Reference space
+        final_transform,
+        sitk.sitkLinear,  # Interpolation method for images
+        0.0,  # Default pixel value for regions outside original image
+        moving_image.GetPixelID()
+    )
+
+    # Apply the transformation to the multi-label moving mask if given
+    registered_moving_mask = None
+    if moving_mask is not None:
+        registered_moving_mask = sitk.Resample(
+            moving_mask,
+            fixed_image,  # Reference space
+            final_transform,
+            sitk.sitkNearestNeighbor,  # Nearest neighbor to preserve labels
+            0,  # Background value remains 0
+            moving_mask.GetPixelID()
+        )
+
+    return final_transform, registered_moving_image, registered_moving_mask
 
 def as_closest_canonical_nifti(path_in, path_out):
     """

From cb188805823ec36e7f0d36e95ee32e8c0b54f7ba Mon Sep 17 00:00:00 2001
From: Yumeng Zhang <62467045+YumengggZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 08:37:32 -0700
Subject: [PATCH 16/20] Update tool.py

---
 ImageTool/tool.py | 90 ++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 77 insertions(+), 13 deletions(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 0e41721..19c8640 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -1,24 +1,29 @@
-import SimpleITK as sitk
+# Core imaging and display libraries
+import SimpleITK as sitk # For medical image I/O and registration
+import ants  # Advanced Normalization Tools for image registration
+import nibabel as nib # For working with NIfTI format
+import nibabel.processing
+from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
+
+# Visualization and interactivity
 import ipywidgets as widgets
 from IPython.display import display
-import numpy as np
 from ipywidgets import interact
 from ipywidgets.widgets import IntSlider
 import matplotlib.pyplot as plt
 import matplotlib
+
+# General utilities
+import numpy as np
 import cv2
 import os 
 import shutil
-from skimage.metrics import structural_similarity 
+from skimage.metrics import structural_similarity  # For SSIM calculation
 from scipy import ndimage
-import ants
+
+# MONAI: Medical imaging deep learning utilities
 import monai
 import monai.metrics
-import nibabel as nib
-import nibabel.processing
-from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
-
-import SimpleITK as sitk
 
 def demons_registration(
     fixed_image, moving_image, fixed_mask=None, moving_mask=None, fixed_points=None, moving_points=None
@@ -105,16 +110,22 @@ def demons_registration(
         )
 
     return final_transform, registered_moving_image, registered_moving_mask
+# ============================
+# Orientation and NIfTI Tools
+# ============================
 
 def as_closest_canonical_nifti(path_in, path_out):
     """
-    Convert the given nifti file to the closest canonical nifti file.
+    Converts a NIfTI image to the closest canonical orientation (RAS).
+    
+    Parameters:
+        path_in (str): Input NIfTI file path.
+        path_out (str): Output file path to save the reoriented image.
     """
     img_in = nib.load(path_in)
     img_out = nib.as_closest_canonical(img_in)
     nib.save(img_out, path_out)
 
-
 def undo_canonical(img_can, img_orig):
     img_ornt = io_orientation(img_orig.affine)
     ras_ornt = axcodes2ornt("RAS")
@@ -129,12 +140,31 @@ def undo_canonical(img_can, img_orig):
 
 
 def undo_canonical_nifti(path_in_can, path_in_orig, path_out):
+    """
+    Reverts a canonicalized NIfTI image back to the original orientation.
+
+    Parameters:
+        path_in_can (str): Canonicalized NIfTI path.
+        path_in_orig (str): Original NIfTI path (provides orientation reference).
+        path_out (str): Output file path for reverted image.
+    """
     img_can = nib.load(path_in_can)
     img_orig = nib.load(path_in_orig)
     img_out = undo_canonical(img_can, img_orig)
     nib.save(img_out, path_out)
 	
 def compute_hu_distance(n_classes, y_pred, y):
+    """
+    Computes Hausdorff distance between predicted and ground truth labels.
+
+    Parameters:
+        n_classes (int): Number of label classes (excluding background).
+        y_pred (ndarray): Predicted label volume.
+        y (ndarray): Ground truth label volume.
+
+    Returns:
+        List of Hausdorff distances for each class.
+    """
     data = []
     for i in range(1, n_classes):
         data.append(monai.metrics.compute_hausdorff_distance((y_pred == i), (y == i), include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None))
@@ -143,6 +173,18 @@ def compute_hu_distance(n_classes, y_pred, y):
     
 
 def get_HU_error(y_pred, y, onehot = False, n_classes = None):
+    """
+    Computes Hausdorff distance with optional one-hot encoding support.
+
+    Parameters:
+        y_pred (ndarray): Predicted labels or one-hot encoded array.
+        y (ndarray): Ground truth labels or one-hot encoded array.
+        onehot (bool): Whether input arrays are already one-hot encoded.
+        n_classes (int): Number of classes, required if onehot=False.
+
+    Returns:
+        Hausdorff distance (tensor).
+    """
     def get_onehot(label, n_classes):
         one_hot = np.eye(n_classes)[label]  # Shape: (1, 32, 32, 32, 6)
         one_hot = one_hot.transpose(3, 0, 1, 2)
@@ -153,10 +195,23 @@ def get_onehot(label, n_classes):
     if onehot is False:
         y = get_onehot(y, n_classes)  # Shape: (1, 32, 32, 32, 6)
         y_pred = get_onehot(y_pred, n_classes) 
-    return monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None)    
-    
+    return monai.metrics.compute_hausdorff_distance(y_pred, y, include_background=False, distance_metric='euclidean', percentile=None, directed=False, spacing=None) 
+	
+# ============================
+# Morphological Operations
+# ============================    
     
 def erode3d(mask=np.ones((6, 6, 6)), size=2):
+    """
+    Perform 3D binary erosion on a mask.
+
+    Parameters:
+        mask (ndarray): 3D binary mask.
+        size (int): Radius of erosion kernel.
+
+    Returns:
+        ndarray: Eroded mask.
+    """
     # Ensure mask is boolean
     mask = mask[:]
     
@@ -169,6 +224,15 @@ def erode3d(mask=np.ones((6, 6, 6)), size=2):
     return eroded_mask
 
 def plot3d(CFR_crop, mask = None, vmax = 2, sample_rate = 5):
+    """
+    Plot 3D scatter of volume data optionally filtered by a binary mask.
+
+    Parameters:
+        volume (ndarray): 3D scalar volume.
+        mask (ndarray): Optional binary mask.
+        vmax (float): Color scale maximum.
+        sample_rate (int): Sampling rate for plotting.
+    """
     matplotlib.use('module://ipympl.backend_nbagg')
     fig = plt.figure(figsize=(8, 8))
     ax = fig.add_subplot(111, projection="3d")

From c1f6f3f0053ca9d91b1a78716ec1ac42dc99347b Mon Sep 17 00:00:00 2001
From: Yumeng Zhang <62467045+YumengggZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 08:39:01 -0700
Subject: [PATCH 17/20] Update tool.py

---
 ImageTool/tool.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 19c8640..659bc2d 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -393,8 +393,6 @@ def plot_mask(mask_for_cv, ax, color = "Blues", alpha = 0.5, label = None):
 
 
 def list_display(img_list, name = "", vmax = 300, cmap = 'jet', read_dcm = False):
-
-    
     if read_dcm:
         img_list = [sitk.GetArrayFromImage(sitk.ReadImage(dcm_file)) for dcm_file in img_list]
     max_slices_contrast = max(img_list, key = lambda x: x.shape[2]).shape[2]
@@ -429,6 +427,7 @@ def update(contrast_slice_index):
         display_slice(contrast_slice_index, img_list, name)
 
     widgets.interact(update, contrast_slice_index=contrast_slice_slider)
+	
 def display_slice(contrast_slice_index, img_list, name):
     n = len(img_list)
     fig, axes = plt.subplots(1, n, figsize=(6*n, 6))
@@ -459,6 +458,7 @@ def lists_display(img_lists, cmap = 'jet', name = None, titles = None):
 
     contrast_slice_slider = widgets.IntSlider(min=0, max=max_slices_contrast-1, step=1, value=0, description='Slice:')
     list_idx_slider = widgets.IntSlider(min=0, max=max_num-1, step=1, value=0, description='img_idx:')
+	
     def display_slice(contrast_slice_index, list_idx_slider):
         n = max(len(img_lists), 2)
         fig, axes = plt.subplots(1, n, figsize=(6*n, 6))

From b33cfc23f1cc5e89551f93c34dab949eecc5b263 Mon Sep 17 00:00:00 2001
From: Yumeng Zhang <62467045+YumengggZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 08:47:43 -0700
Subject: [PATCH 18/20] Update tool.py

---
 ImageTool/tool.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 659bc2d..872595a 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -393,6 +393,16 @@ def plot_mask(mask_for_cv, ax, color = "Blues", alpha = 0.5, label = None):
 
 
 def list_display(img_list, name = "", vmax = 300, cmap = 'jet', read_dcm = False):
+    """
+    Display multiple 3D volumes side by side in a Jupyter widget with slice slider.
+
+    Parameters:
+        img_list (list): List of either file paths (if read_dcm=True) or numpy arrays.
+        name (str): Title prefix for displayed figures.
+        vmax (int): Max intensity for color scaling.
+        cmap (str): Matplotlib colormap.
+        read_dcm (bool): Whether img_list contains paths to DICOM/NIfTI files.
+    """
     if read_dcm:
         img_list = [sitk.GetArrayFromImage(sitk.ReadImage(dcm_file)) for dcm_file in img_list]
     max_slices_contrast = max(img_list, key = lambda x: x.shape[2]).shape[2]

From 59d5e992f935f1b2a8658fb4182d88ccf95e43ed Mon Sep 17 00:00:00 2001
From: Yumeng Zhang <62467045+YumengggZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 09:03:29 -0700
Subject: [PATCH 19/20] Update tool.py

---
 ImageTool/tool.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/ImageTool/tool.py b/ImageTool/tool.py
index 872595a..5939609 100644
--- a/ImageTool/tool.py
+++ b/ImageTool/tool.py
@@ -425,7 +425,14 @@ def update(contrast_slice_index):
 
 
 def folders_display(img_list, name = "", read_dcm = False):
+    """
+    Display a list of 3D volumes from folders or numpy arrays using axial slice navigation.
 
+    Parameters:
+        img_list (list): List of volume paths or numpy arrays.
+        name (str): Figure title prefix.
+        read_dcm (bool): If True, will read images from DICOM/NIfTI files.
+    """
     if read_dcm:
         img_list = [sitk.GetArrayFromImage(sitk.ReadImage(dcm_file)) for dcm_file in img_list]
     max_slices_contrast = max(img_list, key = lambda x: x.shape[0]).shape[0]
@@ -439,6 +446,14 @@ def update(contrast_slice_index):
     widgets.interact(update, contrast_slice_index=contrast_slice_slider)
 	
 def display_slice(contrast_slice_index, img_list, name):
+    """
+    Helper function to plot one slice across multiple volumes.
+
+    Parameters:
+        contrast_slice_index (int): Index of the slice to display.
+        img_list (list): List of 3D numpy arrays.
+        name (str): Figure title prefix.
+    """
     n = len(img_list)
     fig, axes = plt.subplots(1, n, figsize=(6*n, 6))
     fig.suptitle(name + f'  Slice {contrast_slice_index}')
@@ -450,6 +465,15 @@ def display_slice(contrast_slice_index, img_list, name):
 
 
 def lists_display(img_lists, cmap = 'jet', name = None, titles = None):
+    """
+    Display interactive visualization for multiple lists of image volumes.
+
+    Parameters:
+        img_lists (list): Nested list of images or paths. Outer list is category/group.
+        cmap (str): Matplotlib colormap.
+        name (str): Optional overall title.
+        titles (list): Optional list of titles per group.
+    """
     if name == None:
         name = ""
     if titles == None:

From e53700347317a909d702a0bc7c87fa5402bbdf68 Mon Sep 17 00:00:00 2001
From: Yumeng Zhang <62467045+YumengggZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 09:13:43 -0700
Subject: [PATCH 20/20] Update README.md

---
 README.md | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e7aa5a7..56eee12 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,28 @@ plot_mask(mask_for_cv, ax, color = "Blues", alpha = 0.5, label = None):
 ```
 plot the mask inside the mask_for_cv to matplotlib ax. The mask should be binary.
 ```
-interactive_display(img_list, name = ""):
+list_display(img_list, name="", ...)
 ```
-Visualization for all 3d images in img_list by slice. Notice all image should have the same number of slices. Name is the title
+Visualize corresponding slices across multiple 3D volumes side-by-side in an interactive slider-based widget, supporting DICOM and NIfTI formats.
+```
+folders_display(img_list, name="", ...)
+```
+Similar to list_display, but assumes each item is a full 3D volume and displays axial slices interactively for folder-based batch inspection.
+```
+plot3d(volume, mask=None, ...)
+```
+Plots a 3D scatter of intensity values from a volume, optionally masked and downsampled, providing quick insight into spatial distribution.
+```
+load_2d_3d(dicom_input, output_path=None)
+```
+Converts a DICOM series or list of slices into a single 3D volume, enabling standardized volumetric analysis.
+```
+load_3d_2d(dcm_file, output_path)
+```
+Slices a 3D DICOM volume into 2D NIfTI slices while preserving correct spatial metadata (origin, spacing).
+```
+erode3d(mask, size=2)
+```
+Performs 3D binary erosion on segmentation masks to refine region boundaries or isolate structures.
+
+