End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
This addresses the challenging problem of automated kidney tumor segmentation for medical imaging applications, representing an incremental improvement in a specific domain.
The paper tackled kidney and tumor segmentation in medical imaging by proposing a cascaded 3D U-Net method, achieving a Sørensen-Dice coefficient of 0.902 for kidney and 0.408 for tumor segmentation in a five-fold cross-validation on 210 patients.
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.