This study aimed to investigate the effect of UNet++ compression with pruning and principal component analysis (PCA) for kidney and cyst image segmentation of Autosomal Dominant Polycystic Kidney Disease (ADPKD). We used 756 T2-weighted MRI images of kidneys with ADPKD among the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort. The UNet++, UNet++ with prune compression (prUNet++), and UNet++ with PCA compression (PCAUNet++) were trained, validated, and tested with 604, 76, and 76 kidney images, respectively. The model performance was analyzed using the Dice similarity coefficient (DSC). The training time per epoch for UNet++ was 217±5s and 220±7s respectively for kidneys and cysts. This was used as a pre-trained model for PCAUNet++ and prUNet++ whose training times after compression for kidneys and cysts respectively were 90±3s, 109±6s and, 110±2s, 124±4s. (p<0.0001). The test dataset’s average DSC values for UNet++, prUNet++, and PCAUNet++ were 0.93±0.05, 0.93±0.08, and 0.93±0.07, respectively, for kidney segmentation, while those for cyst segmentation were 0.87±0.04, 0.84±0.08, and 0.84±0.09, respectively, without statistical difference over the three models suggesting the pending rejection of the null hypothesis (p≥0.05). We demonstrated that compression techniques can be employed in segmentation tasks without the need to train a model from scratch and use an already trained model with additional little training, avoiding computational complexity and large training time. PCA-based compression technique significantly decreased the post-training time of UNet++ compared to prUNet++ without a significant reduction in the accuracy of kidney and cyst segmentation for ADPKD.