Renal parenchyma segmentation based on a cascaded self-adaptive framework with local context-aware mix-up regularization in abdominal MR images

Abstract

Segmentation of the renal parenchyma responsible for a renal function is necessary for surgical planning and decisionmaking of renal partial nephrectomy (RPN) by identifying the correlation between the renal parenchyma volume and renal function after RPN on abdominal magnetic resonance (MR) images without radiation exposure. This paper proposes a cascaded self-adaptive framework that uses local context-aware mix-up regularization on abdominal MR images acquired from multiple devices. The proposed renal parenchyma segmentation network consists of two stages: kidney bounding volume extraction and renal parenchyma segmentation. Before kidney bounding volume extraction, self-adaptive normalization is performed using nnU-Net as the backbone network to reduce differences in signal intensity and pixel spacing among MR images of different intensity ranges acquired from multiple MR devices. In the kidney bounding volume extraction stage, the renal parenchyma area is segmented using 3D U-Net with low-resolution data down-sampled twice from the original to efficiently localize the kidney in the abdomen. Bounding volume is generated to focus on the renal parenchyma area during the renal parenchyma segmentation stage by cropping to the volume-of-interest region using the segmentation results up-sampled to the original resolution. In the segmentation stage, the renal parenchyma is segmented using 3D U-Net with mix-up augmented bounding volume to improve the regularization performance of the model. The average F1-score of our method was 92.27%, which was 3.07%p and 0.32%p higher than the segmentation method using original 3D cascaded nnU-Net and 3D cascaded nnU-Net with kidney bounding volume extraction, respectively.