Generalizable Kidney Segmentation for Total Volume Estimation
摘要
We introduce a deep learning approach for automated kidney segmentation in autosomal dominant polycystic kidney disease (ADPKD). Our method combines Nyul normalization, resampling, and attention mechanisms to create a generalizable network. We evaluated our approach on two distinct datasets and found that our proposed model outperforms the baseline method with an average improvement of 9.45 % in Dice and 79.90 % in mean surface symmetric distance scores across both the datasets, demonstrating its potential for robust and accurate total kidney volume calculation from T1-w MRI images in ADPKD patients.