- Overview
- Hardware Requirements
- Installation (command-line based)
- Installation (browser based)
- Download Data
- Revisions
- Quickstart
- Smart Interpolation
- Deep Learning
- Mesh Generator
- Biomedisa Features
- Authors
- FAQ
- Citation
- License
Biomedisa (https://biomedisa.info) is a free and easy-to-use open-source application for segmenting large volumetric images, e.g. CT and MRI scans, developed at The Australian National University CTLab. Biomedisa's semi-automated segmentation is based on a smart interpolation of sparsely pre-segmented slices, taking into account the complete underlying image data. In addition, Biomedisa enables deep learning for the fully automated segmentation of series of similar samples. It can be used in combination with segmentation tools such as Amira/Avizo, ImageJ/Fiji and 3D Slicer. If you are using Biomedisa or the data for your research please cite: Lösel, P.D. et al. Introducing Biomedisa as an open-source online platform for biomedical image segmentation. Nat. Commun. 11, 5577 (2020).
- One or more NVIDIA GPUs with compute capability 3.0 or higher or an Intel CPU
- Ubuntu 22.04 + CUDA + GPU (recommended)
- Ubuntu 22.04 + OpenCL + CPU (smart interpolation only and very slow)
- Windows 10 + CUDA + GPU (recommended)
- Windows 10 + OpenCL + GPU (easy to install but lacks features like allaxis, smoothing, uncertainty, optimized GPU memory usage)
- Windows 10 + OpenCL + CPU (very slow)
- Download test data from our gallery
24.5.22
- Pip is the preferred installation method
- Commands, module names and imports have been changed to conform to the Pip standard
- For versions <=23.9.1 please check README
Install the Biomedisa package from the Python Package Index:
python -m pip install -U biomedisa
For smart interpolation and deep Learning modules, follow the installation instructions above.
from biomedisa.features.biomedisa_helper import load_data, save_data
from biomedisa.interpolation import smart_interpolation
# load data
img, _ = load_data('Downloads/trigonopterus.tif')
labels, header = load_data('Downloads/labels.trigonopterus_smart.am')
# run smart interpolation with optional smoothing result
results = smart_interpolation(img, labels, smooth=100)
# get results
regular_result = results['regular']
smooth_result = results['smooth']
# save results
save_data('Downloads/final.trigonopterus.am', regular_result, header=header)
save_data('Downloads/final.trigonopterus.smooth.am', smooth_result, header=header)python -m biomedisa.interpolation C:\Users\%USERNAME%\Downloads\tumor.tif C:\Users\%USERNAME%\Downloads\labels.tumor.tif
If pre-segmentation is not exclusively in the XY plane:
python -m biomedisa.interpolation C:\Users\%USERNAME%\Downloads\tumor.tif C:\Users\%USERNAME%\Downloads\labels.tumor.tif --allaxis
from biomedisa.features.biomedisa_helper import load_data
from biomedisa.deeplearning import deep_learning
# load image data
img1, _ = load_data('Head1.am')
img2, _ = load_data('Head2.am')
img_data = [img1, img2]
# load label data and header information to be stored in the network file (optional)
label1, _ = load_data('Head1.labels.am')
label2, header, ext = load_data('Head2.labels.am',
return_extension=True)
label_data = [label1, label2]
# load validation data (optional)
img3, _ = load_data('Head3.am')
img4, _ = load_data('Head4.am')
label3, _ = load_data('Head3.labels.am')
label4, _ = load_data('Head4.labels.am')
val_img_data = [img3, img4]
val_label_data = [label3, label4]
# deep learning
deep_learning(img_data, label_data, train=True, batch_size=12,
val_img_data=val_img_data, val_label_data=val_label_data,
header=header, extension=ext, path_to_model='honeybees.h5')Start training with a batch size of 12:
python -m biomedisa.deeplearning C:\Users\%USERNAME%\Downloads\training_heart C:\Users\%USERNAME%\Downloads\training_heart_labels -t -bs=12
Monitor training progress using validation data:
python -m biomedisa.deeplearning C:\Users\%USERNAME%\Downloads\training_heart C:\Users\%USERNAME%\Downloads\training_heart_labels -t -vi=C:\Users\%USERNAME%\Downloads\val_img -vl=C:\Users\%USERNAME%\Downloads\val_labels
If running into ResourceExhaustedError due to out of memory (OOM), try to use a smaller batch size.
from biomedisa.features.biomedisa_helper import load_data, save_data
from biomedisa.deeplearning import deep_learning
# load data
img, _ = load_data('Head5.am')
# deep learning
results = deep_learning(img, predict=True,
path_to_model='honeybees.h5', batch_size=6)
# save result
save_data('final.Head5.am', results['regular'], results['header'])python -m biomedisa.deeplearning C:\Users\%USERNAME%\Downloads\testing_axial_crop_pat13.nii.gz C:\Users\%USERNAME%\Downloads\heart.h5 -p
Create STL mesh from segmentation (label values are saved as attributes)
from biomedisa.features.biomedisa_helper import load_data, save_data
from biomedisa.mesh import get_voxel_spacing, save_mesh
# load segmentation
data, header, extension = load_data('final.Head5.am', return_extension=True)
# get voxel spacing
x_res, y_res, z_res = get_voxel_spacing(header, extension)
print(f'Voxel spacing: x_spacing, y_spacing, z_spacing = {x_res}, {y_res}, {z_res}')
# save stl file
save_mesh('final.Head5.stl', data, x_res, y_res, z_res, poly_reduction=0.9, smoothing_iterations=15)python -m biomedisa.mesh 'final.Head5.am'
For DICOM, PNG files, or similar formats, file path must reference either a directory or a ZIP file containing the image slices.
from biomedisa.features.biomedisa_helper import load_data, save_data
# load data as numpy array
data, header = load_data('temp.tif')
# save data (for TIFF, header=None)
save_data('temp.tif', data, header)from biomedisa.features.biomedisa_helper import img_resize
# resize image data
zsh, ysh, xsh = data.shape
new_zsh, new_ysh, new_xsh = zsh//2, ysh//2, xsh//2
data = img_resize(data, new_zsh, new_ysh, new_xsh)
# resize label data
label_data = img_resize(label_data, new_zsh, new_ysh, new_xsh, labels=True)from biomedisa.features.biomedisa_helper import clean, fill
# delete outliers smaller than 90% of the segment
label_data = clean(label_data, 0.9)
# fill holes
label_data = fill(label_data, 0.9)from biomedisa.features.biomedisa_helper import Dice_score, ASSD
dice = Dice_score(ground_truth, result)
assd = ASSD(ground_truth, result)- Philipp D. Lösel
See also the list of contributors who participated in this project.
Frequently asked questions can be found at: https://biomedisa.info/faq/.
If you use Biomedisa or the data, please cite the following paper:
Lösel, P.D. et al. Introducing Biomedisa as an open-source online platform for biomedical image segmentation. Nat. Commun. 11, 5577 (2020). https://doi.org/10.1038/s41467-020-19303-w
If you use Biomedisa's Deep Learning, you may also cite:
Lösel, P.D. et al. Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning. PLoS Comput. Biol. 19, e1011529 (2023). https://doi.org/10.1371/journal.pcbi.1011529
If you use Biomedisa's Smart Interpolation, you can also cite the initial description of this method:
Lösel, P. & Heuveline, V. Enhancing a diffusion algorithm for 4D image segmentation using local information. Proc. SPIE 9784, 97842L (2016). https://doi.org/10.1117/12.2216202
This project is covered under the EUROPEAN UNION PUBLIC LICENCE v. 1.2 (EUPL).
