Automatic Abdominal Multi Organ Segmentationusing Residual UNet

Murugesan, G. K.; McCrumb, D.; Brunner, E.; Kumar, J.; Soni, R.; Grigorash, V.; Chang, A.; Peck, A.; VanOss, J.; Moore, S.

doi:10.1101/2023.02.15.528755

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bioRxiv · 10.1101/2023.02.15.528755

Automatic Abdominal Multi Organ Segmentationusing Residual UNet

Murugesan, G. K.·McCrumb, D.·Brunner, E.·Kumar, J.·Soni, R.·Grigorash, V.·Chang, A.·Peck, A.·VanOss, J.·Moore, S.

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Abstract

Automated segmentation of abdominal organs plays an important role in supporting computer-assisted diagnosis, radiotherapy, biomarker extraction, surgery navigation, and treatment planning. Segmenting multiple abdominal organs using a single algorithm would improve model development efficiency and accelerate model deployment into clinical workflows. To achieve broadly generalized performance, we trained a residual UNet using 500 CT/MRI scans collected from multi-center, multi-vendor, multi-phase, multi-disease patients, each with voxel-level annotation of 15 abdominal organs. Using the model trained on multimodality (CT/MRI), we achieved an average dice of 0.8990 in the held-out test dataset with only CT scans (N=100). An average dice of 0.8948 was achieved in the held-out test dataset with both CT and MRI scans (N=120. Our results demonstrate broad generalization of the model.

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Murugesan, G. K., McCrumb, D., Brunner, E., Kumar, J., Soni, R., Grigorash, V., Chang, A., Peck, A., VanOss, J., Moore, S.. 2023-02-16. Automatic Abdominal Multi Organ Segmentationusing Residual UNet. https://doi.org/10.1101/2023.02.15.528755

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