LinFlo-Net Pre-trained Weights This record provides pre-trained PyTorch model weights for LinFlo-Net, a deep learning method for automatically generating simulation-ready 3D whole-heart meshes from cardiac CT and MR images. Contents The archive contains a single PyTorch checkpoint (`best_model.pth`) for inference with the LinFlo-Net pipeline. The same checkpoint is used for both CT and MR inputs; set the modality at inference time with `--modality ct` or `--modality mr`. The checkpoint corresponds to the best validation model from training the full LinFlo-Net architecture (linear transform + flow deformation with signed-distance supervision). It is intended for use with the official LinFlo-Net software and bundled template mesh. What the models do Given a 3D cardiac image and a template heart mesh, LinFlo-Net predicts a patient-specific deformed mesh (`.vtp`) and an associated segmentation rasterized to image space. The models were developed for whole-heart meshing workflows, including computational modeling and simulation preparation. Training data Models were trained on the Multi-Modality Whole Heart Segmentation (MMWHS) challenge dataset, with data augmentation following the MeshDeformNet procedure. CT and MR models were trained separately with modality-appropriate preprocessing and normalization. Training data are **not** included in this record. MMWHS must be obtained separately from the dataset providers. Software requirements Use these weights with the LinFlo-Net package: - PyPI: https://pypi.org/project/linflonet/- Source code: https://github.com/ArjunNarayanan/LinFlo-Net- Quick start: https://github.com/ArjunNarayanan/LinFlo-Net/blob/main/docs/quick_start.md Example inference: pip install linflonet linflonet predict \ --image /path/to/scan.nii.gz \ --model /path/to/best_model.pth \ --modality ct \ --output /path/to/output