Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study
Piotr Woźnicki, Florian Siedek, Maatje D.A. van Gastel, Daniel Pinto dos Santos, Sita Arjune, Larina A. Karner, Franziska Meyer, Liliana Caldeira, Thorsten Persigehl, Ron T. Gansevoort, Franziska Grundmann, Bettina Baeßler, Roman‐Ulrich Müller
Abstract
Key Points We developed a model for automated kidney and liver volumetry in ADPKD to provide assistance with time-consuming volumetry. The model works in both coronal and axial planes and was tested in the real-life setting using large multicentric cohorts. The trained model is published along with the code to allow for further joint development and integration into commercial software packages. Background Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting. Methods The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model’s performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients. Results The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92–0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996–0.999) with low bias and high precision (−0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of −0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of −1%±7%. Conclusions Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible. Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521