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--- |
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license: apache-2.0 |
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language: |
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- en |
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task_categories: |
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- image-classification |
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tags: |
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- medical |
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- brain-data |
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- mri |
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pretty_name: 3D Brain Structure MRI PCA |
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--- |
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## 🧠 Model Summary |
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# brain2vec_PCA |
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A linear PCA model for brain structure T1 MRIs. The models takes in a 3d MRI NIfTI file and compresses to 1200 latent dimensions before reconstructing the image. |
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# Training data |
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[radiata-ai/brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure): 3066 scans from 2085 individuals in the 'train' split. Mean age = 45.1 +- 24.5, including 2847 scans from cognitively normal subjects and 219 scans from individuals with an Alzheimer's disease clinical diagnosis. |
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# Example usage |
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``` |
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# get brain2vec_PCA model repository |
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git clone https://huggingface.co/radiata-ai/brain2vec_PCA |
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cd brain2vec_PCA |
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# pull pre-trained model weights |
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sudo apt install git-lfs |
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git lfs install |
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git lfs pull |
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# set up virtual environemt |
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python3 -m venv venv_brain2vec_PCA |
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source venv_brain2vec_PCA/bin/activate |
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# install Python libraries |
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pip install -r requirements.txt |
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# create the csv file inputs.csv listing the scan paths and other info |
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# this script loads the radiata-ai/brain-structure dataset from Hugging Face |
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python create_csv.py |
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mkdir pca_output |
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# train the model |
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nohup python train_brain2vec_PCA.py \ |
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--inputs_csv inputs.csv \ |
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--output_dir ./pca_output \ |
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--pca_type standard \ |
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--n_components 1200 \ |
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> train_log.txt 2>&1 & |
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# model inference |
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# for a set of scans in inputs.csv |
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python inference_brain2vec_PCA.py \ |
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--pca_model pca_model.joblib \ |
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--csv_input inputs.csv \ |
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--output_dir pca_output \ |
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--embeddings_filename pca_embeddings_all |
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# or for individual scans |
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python inference_brain2vec_PCA.py \ |
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--pca_model pca_model.joblib \ |
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--input_images /path/to/img1.nii.gz /path/to/img2.nii.gz \ |
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--output_dir pca_output \ |
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--embeddings_filename pca_embeddings_2 \ |
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--save_recons |
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``` |
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# Methods |
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Input scan image dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see [radiata-ai/brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure)). |
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The image transform crops to 80 x 96 x 80, 2mm^3 resolution, and scales image intensity to range [0,1]. Images are flattened to 614400-length 1D vectors. |
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PCA is performed using [sklearn.decomposition.PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html). |
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# Citation |
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``` |
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@misc{Radiata-Brain2Vec-PCA, |
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author = {Jesse Brown and Clayton Young}, |
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title = {brain2vec_PCA: A Linear PCA Model for Brain Structure T1 MRIs}, |
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year = {2025}, |
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url = {https://huggingface.co/radiata-ai/brain2vec_PCA}, |
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note = {Version 1.0}, |
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publisher = {Hugging Face} |
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} |
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``` |
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# License |
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### Apache License 2.0 |
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Copyright 2025 Jesse Brown |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at: |
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[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0) |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License. |
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