• Corpus ID: 239050547

OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data

@article{Reich2021OSSNetME,
  title={OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data},
  author={Christoph Reich and Tim Prangemeier and Ozdemir Cetin and Heinz Koeppl},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10640}
}
Convolutional neural networks (CNNs) are the current state-of-the-art meta-algorithm for volumetric segmentation of medical data, for example, to localize COVID-19 infected tissue on computer tomography scans or the detection of tumour volumes in magnetic resonance imaging. A key limitation of 3D CNNs on voxelised data is that the memory consumption grows cubically with the training data resolution. Occupancy networks (O-Nets) are an alternative for which the data is represented continuously in… 

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