The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

ABSTRACT: We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m²) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m²). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD group data and subsequently on the combined data of the RD and non-RD groups. For bilateral kidney segmentation of the non-RD group, the best performance was obtained when using IP image, with a Dice score of 0.902 ± 0.034, average surface distance of 1.46 ± 0.75 mm, and a difference of - 27 ± 21 mL be

SUBMITTER: Inoue K 

PROVIDER: S-EPMC10575938 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature