Transfer Learning with Multi-Sequence MRI for Segmentation of Autosomal Dominant Polycystic Kidney Disease Using U-Net

In recent studies, the measurement of total kidney volume, a primary indicator for the diagnosis and treatment of renal diseases, has been advanced through artificial-intelligence-driven automated segmentation. However, the limited quantity of medical data remains a persistent challenge, with its scarcity negatively impacting the outcomes of machine learning algorithms. In this study, we have enhanced the accuracy of machine learning for disease diagnosis by employing various MRI sequences commonly used during renal imaging. We created a model for kidney segmentation using U-Net and performed single training, joint training, and transfer learning using MRI images from two sequences based on SSFP and SSFSE. Ultimately, during transfer learning, we achieved the highest accuracy with a Dice coefficient of 0.951 and a mean difference of 2.05% (-3.47%, 7.57%) in Bland-Altman analysis for SSFP. Similarly, for SSFSE, we obtained a Dice coefficient of 0.952 and a mean difference of 4.33% (-7.05%, 15.71%) through Bland-Altman analysis. This demonstrates our ability to enhance prediction accuracy for each sequence by leveraging different sequences.