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merged 4 commits into from
Jul 25, 2025
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72 feature update brats tutorial #73

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e424767
Improve segmentation visualization, typing and docstrings
MarcelRosier Jul 25, 2025
4f3b684
Rerun for latest version and showcase logging utils
MarcelRosier Jul 25, 2025
8a1b221
Update BraTS/utils.py
neuronflow Jul 25, 2025
c5627bd
Update BraTS/utils.py
neuronflow Jul 25, 2025
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273 changes: 61 additions & 212 deletions BraTS/tutorial.ipynb
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42 changes: 27 additions & 15 deletions BraTS/utils.py
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Original file line number Diff line number Diff line change
@@ -1,19 +1,22 @@
from pathlib import Path
from typing import Union

import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np

DATA_FOLDER = "data"
DATA_FOLDER = Path("data")


def visualize_segmentation_data(
data_folder: str = DATA_FOLDER,
data_folder: Union[str, Path] = DATA_FOLDER,
subject_id: str = "BraTS-GLI-00001-000",
slice_index: int = 75,
):
"""Visualize the MRI modalities for a given slice index

Args:
data_folder (str, optional): Path to the folder containing the t1, t1c, t2 & flair file. Defaults to DATA_FOLDER.
data_folder (Union[str, Path], optional): Path to the folder containing the t1, t1c, t2 & flair file. Defaults to DATA_FOLDER.
slice_index (int, optional): Slice to be visualized (first index in data of shape (155, 240, 240)). Defaults to 75.
"""
_, axes = plt.subplots(1, 4, figsize=(12, 10))
Expand All @@ -29,14 +32,14 @@ def visualize_segmentation_data(


def visualize_inpainting_data(
data_folder: str = DATA_FOLDER,
data_folder: Union[str, Path] = DATA_FOLDER,
subject_id: str = "BraTS-GLI-00001-000",
slice_index: int = 75,
):
"""Visualize the MRI modalities for a given slice index

Args:
data_folder (str, optional): Path to the folder containing the t1n and mask files. Defaults to DATA_FOLDER.
data_folder (Union[str, Path], optional): Path to the folder containing the t1n and mask files. Defaults to DATA_FOLDER.
slice_index (int, optional): Slice to be visualized (first index in data of shape (155, 240, 240)). Defaults to 75.
"""
_, axes = plt.subplots(1, 2, figsize=(6, 10))
Expand All @@ -51,32 +54,39 @@ def visualize_inpainting_data(
axes[i].axis("off")


def visualize_segmentation(modality_file: str, segmentation_file: str):
def visualize_segmentation(
modality_file: Union[str, Path], segmentation_file: Union[str, Path]
):
"""Visualize the MRI modality and the segmentation

Args:
modality_file (str): Path to the desired modality file
segmentation_file (str): Path to the segmentation file
modality_file (Union[str, Path]): Path to the desired modality file
segmentation_file (Union[str, Path]): Path to the segmentation file
"""
modality_np = nib.load(modality_file).get_fdata().transpose(2, 1, 0)
seg_np = nib.load(segmentation_file).get_fdata().transpose(2, 1, 0)

_, ax = plt.subplots(1, 2, figsize=(8, 4))

slice_index = modality_np.shape[0] // 2 # You can choose any slice here

# Mask out background (0) in the segmentation
seg_slice = seg_np[slice_index, :, :]
ax[0].imshow(modality_np[slice_index, :, :], cmap="gray")
ax[1].imshow(modality_np[slice_index, :, :], cmap="gray")
ax[1].imshow(seg_np[slice_index, :, :], cmap="plasma", alpha=0.3)
ax[1].imshow(seg_slice, cmap="plasma", alpha=np.where(seg_slice > 0, 0.3, 0))

Comment on lines 75 to +78
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Copilot AI Jul 25, 2025

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The np.where operation is computed for every pixel on each function call. Consider pre-computing the alpha mask: alpha_mask = np.where(seg_slice > 0, 0.3, 0) then use alpha=alpha_mask for better performance.

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for ax in ax:
ax.axis("off")
plt.tight_layout()


def visualize_inpainting(t1n_voided: str, prediction: str):
def visualize_inpainting(t1n_voided: Union[str, Path], prediction: Union[str, Path]):
"""Visualize the inpainting results

Args:
t1n_voided (str): Voided T1 modality file
prediction (str): Inpainting prediction file
t1n_voided (Union[str, Path]): Voided T1 modality file
prediction (Union[str, Path]): Inpainting prediction file
"""
voided_np = nib.load(t1n_voided).get_fdata().transpose(2, 1, 0)
inpainting_np = nib.load(prediction).get_fdata().transpose(2, 1, 0)
Expand All @@ -91,15 +101,17 @@ def visualize_inpainting(t1n_voided: str, prediction: str):


def visualize_missing_mri_t2w(
synthesized_t2w: str,
data_folder: str = DATA_FOLDER,
synthesized_t2w: Union[str, Path],
data_folder: Union[str, Path] = DATA_FOLDER,
subject_id: str = "BraTS-GLI-00001-000",
slice_index: int = 75,
):
"""Visualize the MRI modalities for a given slice index

Args:
data_folder (str, optional): Path to the folder containing the t1, t1c, t2 & flair file. Defaults to DATA_FOLDER.
synthesized_t2w (Union[str, Path]): Path to the synthesized T2w file
data_folder (Union[str, Path], optional): Path to the folder containing the t1, t1c, t2 & flair file. Defaults to DATA_FOLDER.
subject_id (str, optional): Subject ID to visualize. Defaults to "BraTS-GLI-00001-000".
slice_index (int, optional): Slice to be visualized (first index in data of shape (155, 240, 240)). Defaults to 75.
"""
_, axes = plt.subplots(1, 5, figsize=(12, 10))
Expand Down