Pre-trained Swin UNETR, nnU-Net, and AIMOS Models for Mouse Organ Segmentation
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Description
This repository contains pre-trained Swin UNETR, nnU-Net, and AIMOS models for mouse organ segmentation. These models were trained using native micro-CT and contrast-enhanced micro-CT scans from a publicly available preclinical database. The models are capable of segmenting key abdominal structures, including the bladder, lungs, heart, liver, intestine, kidneys, and spleen.
The public data are openly available at https://www.nature.com/articles/sdata2018294.
The models are based on the Swin UNETR[1], nnU-Net[2], and AIMOS[3] frameworks, with modifications to adapt them to our specific needs.
[1] Hatamizadeh A, Nath V, Tang Y, et al. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images[C]//International MICCAI brainlesion workshop. Cham: Springer International Publishing, 2021: 272-284.
[2] Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature methods, 2021, 18(2): 203-211.
[3] Schoppe O, Pan C, Coronel J, et al. Deep learning-enabled multi-organ segmentation in whole-body mouse scans[J]. Nature communications, 2020, 11(1): 5626.
Files
Mouse Organ Segmentation.zip
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(1.3 GB)
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