arXiv Open Access 2022

Ensembled Autoencoder Regularization for Multi-Structure Segmentation for Kidney Cancer Treatment

David Jozef Hresko Marek Kurej Jakub Gazda Peter Drotar

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Abstrak

The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we combine these two networks, and further boost the performance by mixup augmentation.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (4)

David Jozef Hresko

Marek Kurej

Jakub Gazda

Peter Drotar

Format Sitasi

APA MLA BibTeX

Hresko, D.J., Kurej, M., Gazda, J., Drotar, P. (2022). Ensembled Autoencoder Regularization for Multi-Structure Segmentation for Kidney Cancer Treatment. https://arxiv.org/abs/2208.04007

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓