nnUNet 用于常染色体显性多囊肾病的肾脏和囊肿自动分割。

nnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

机构信息

Departments of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

Department of Nephrology, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

出版信息

Curr Med Imaging. 2024;20:1-9. doi: 10.2174/0115734056272767231130110017.

DOI:10.2174/0115734056272767231130110017

PMID:38389364

Abstract

BACKGROUND

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD.

OBJECTIVE

This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients.

METHODS

756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD).

RESULTS

The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation.

CONCLUSION

The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

摘要

背景

常染色体显性多囊肾病（ADPKD）是一种遗传性疾病，可导致肾脏囊肿不受控制地生长，随着时间的推移导致肾脏体积增大和肾功能丧失。肾脏总体积（TKV）和囊肿负担已被用作 ADPKD 的预后成像生物标志物。

目的

本研究旨在评估 nnUNet 在 ADPKD 患者 T2 加权（T2W）MRI 图像中自动进行肾脏和囊肿分割的能力。

方法

从多囊肾病放射影像学研究联合会（CRISP）队列中的 95 名患者的 756 张肾脏图像中检索数据（95 名患者×2 个肾脏×4 次随访扫描）。nnUNet 模型分别在 604、76 和 76 张图像上进行训练、验证和测试。相比之下，每位患者的所有图像均专门分配到训练、验证或测试集中，以最大程度地减少评估偏差。使用半自动方法定义的肾脏和囊肿区域用作金标准。使用 Dice 相似系数（DSC）、交并比（IoU）评分和 Hausdorff 距离（HD）评估模型性能。