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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 between ground-truth and automatically computed volume. Slightly worse results were obtained for the combined data of the RD and non-RD groups and for unilateral kidney segmentation, particularly when segmenting the right kidney from the OP images. Our 3D CNN-assisted automatic segmentation tools can be utilized in future studies on total kidney volume measurements and various image analyses of a large number of patients with CKD.

SUBMITTER: Inoue K

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

REPOSITORIES: biostudies-literature

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

Inoue Kaiji K Hara Yuki Y Nagawa Keita K Koyama Masahiro M Shimizu Hirokazu H Matsuura Koichiro K Takahashi Masao M Osawa Iichiro I Inoue Tsutomu T Okada Hirokazu H Ishikawa Masahiro M Kobayashi Naoki N Kozawa Eito E

Scientific reports 20231013 1

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 m<sup>2</sup>) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m<sup>2</sup>). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD gro ...[more]

PMID: 37833438

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Dataset Information

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

Publications

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

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