Enhancing Liver Cyst Segmentation for ADPKD Patients Through Deep Learning Assistance

Motivation: Autosomal dominant polycystic kidney disease (ADPKD) often also has polycystic liver disease (PLD), impacting patients' well-being. Manually segmenting liver cysts for measuring disease burden is time-consuming and error-prone, necessitating an automated solution. Goal(s): We introduce a deep-learning (DL) framework for liver cyst segmentation in ADPKD/PLD patients. Approach: An nnUNet-based framework ensembled 2D and 3D models trained on our institute's ADPKD dataset to detect liver cysts in an external test set. Additionally, we implemented patient, cyst, and voxel-level evaluation metrics for clinical impact assessment. Results: Our model achieved an 84% cyst-level Dice score significantly reducing annotation time by 91%. Impact: This research aims to revolutionize PLD monitoring by transitioning from qualitative to quantitative, replicable, and scalable approaches. Advanced DL models can produce high-quality liver cysts annotations and introduce cyst-level evaluation metrics, aiding radiologists with precise disease assessment and clinical decisions.