Abstract
Renal cancer is one of the most prevalent cancers worldwide. Clinical signs of kidney cancer include hematuria and low back discomfort, which are quite distressing to the patient. Due to the rapid growth of artificial intelligence and deep learning, medical image segmentation has evolved dramatically over the past few years. In this paper, we propose modified nn-UNet for kidney multi-structure segmentation. Our solution is founded on the thriving nn-UNet architecture using 3D full resolution U-net. Firstly, various hyperparameters are modified for this particular task. Then, by doubling the number of filters in 3D full resolution nnUNet architecture to achieve a larger network, we may capture a greater receptive field. Finally, we include an axial attention mechanism in the decoder, which can obtain global information during the decoding stage to prevent the loss of local knowledge. Our modified nn-UNet achieves state-of-the-art performance on the KiPA2022 dataset when compared to conventional approaches such as 3D U-Net, MNet, etc.
Abstract (translated)
URL
https://arxiv.org/abs/2208.05241
