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Abstract #2705

Quality Control Pipeline for MRI Annotations of Polycystic Kidneys

Chenglin Zhu1, Usama Satter1, Vahid Bazojoo1, Hreedi Dev1, Yelynn Kim1, Zhongxiu Hu1, Xinzi He1,2, and Martin Prince1,3
1Weill Cornell Medicine, New York City, NY, United States, 2Cornell University, Ithaca, NY, United States, 3Columbia College of Physicians and Surgeons, New York City, NY, United States

Synopsis

Keywords: Kidney, Analysis/Processing, ADPKD

Motivation: Public MRI segmentation models often fail to accurately label polycystic kidneys, reducing total kidney volume measurement accuracy, a key Autosomal Dominant Polycystic Kidney Disease (ADPKD) biomarker.

Goal(s): To create a quality control pipeline and open-source high-quality, voxel-level MRI kidney annotation in ADPKD patients.

Approach: 100 MRIs from 20 ADPKD patients were segmented using a deep learning model, with subsequent human-expert corrections followed by a quality control pipeline, validating labeling consistency, anatomical constraints, and inter-sequence volume/dimensional agreement.

Results: We provide a quality control pipeline for hole-free, stray-voxel-free, largely (93%) inter-sequence-consistent segmentations that complements and improves upon expert annotators of polycystic kidney segmentations on MRI.

Impact: This quality control pipeline with open-source polycystic kidney segmentations aims to enhance future public segmentation models, benefiting ADPKD patients, clinicians, and researchers. The segmentation quality assessment also encourages establishing reporting standards for open-source segmentation datasets.

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Keywords