Deep Learning-Based Instance-Level Segmentation of Kidney and Liver Cysts in CT Images of Patients Affected by Polycystic Kidney Disease.

Background: Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, MRI-based advanced biomarkers like total cyst number (TCN) and cyst parenchyma surface area (CPSA) have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study is to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in CT images.

Methods: Abdominal CTs of ADPKD patients were gathered retrospectively between 2001-2018. A 3D deep-learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the non-cystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in non-contrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy.

Results: Two-hundred CT scans from 148 patients (mean age, 51.2 ± 14.1 years; 48% male) were utilized for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model vs reader: TCNkidney/liver r=0.96/0.97 and CPSAkidney r=0.98), inter-reader: TCNkidney/liver r=0.96/0.98 and CPSAkidney r=0.99).

Conclusions: Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.