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Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI -- Deep learning models

Name: Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI -- Deep learning models
Keywords: Vestibular Schwannoma, Segmentation, Deep Learning, Convolutional Neural Network, Volumetry, Surveillance MRI

appsOther research productkeyboard_double_arrow_right Other ORP type 01 Jan 2023Publisher:Zenodo

Authors: Aaron Kujawa; Dorent, Reuben; Wijethilake, Navodini; Connor, Steve; Thomson, Suki; Ivory, Marina; Bradford, Robert; +5 Authors

doi: 10.5281/zenodo.10363647

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI -- Deep learning models

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Abstract

These are zipped folders containing all models trained with nnU-Net for the journal publication: Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI The Zenodo upload contains the following files: Multi-Centre-Routine-Clinical-(MC-RC)-models.zip Models trained on the MC-RC dataset Single-Centre-Gamma-Knife-(SC-GK)-models.zip Models trained on the SC-GK dataset MC-RC+SC-GK-models.zip Models trained on both datasets example_input_images.zip example images to test the inference To run inference from a Linux command line, follow these steps: 1. install the nnU-Net (v2) python package. This can be done with the following command: pip install nnunetv2 2. unzip the model folders 3. set the environment variable `nUNet_results` to the path that contains the unzipped model folders (e.g. Dataset910_VSMCRCT1, Dataset911_VSMCRCT2, etc.). For example you can use the following command: export nnUNet_results="/home/username/Multi-Centre-Routine-Clinical-(MC-RC)-models/" 4. follow the model-specific instructions under /inference_instructions.txt Make sure to replace INPUT_FOLDER, OUTPUT_FOLDER, etc. in the commands with valid paths. The final post-processing command starting with nnUNetv2_apply_postprocessing should be omitted.

Related Organizations

King's College London
United Kingdom

Keywords

Vestibular Schwannoma, Segmentation, Deep Learning, Convolutional Neural Network, Volumetry, Surveillance MRI

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Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI -- Deep learning models
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