Single Generalized Convolutional Neural Network for Automatic Liver Extraction and R2* Estimation for Iron Overload Assessment

Motivation: Although R2*-MRI is extensively validated to assess hepatic iron content(HIC), different MRI sequences are used, hence multiple sequence-specific convolutional neural networks(CNNs) have been proposed for automated liver segmentation and HIC estimation. Goal(s): Assess feasibility of generalized CNN with limited training datasets to automate liver segmentation across various MRI sequences used to quantify HIC in clinical practice. Approach: Data of twenty-nine patients scanned using multi-echo 2D/3D breath-hold and free-breathing Cartesian and radial GRE sequences were used to train U-Net CNN using incremental learning. Results: Excellent agreement was obtained between manual and single generalized U-Net for liver segmentation and R2* estimation across multiple MRI sequences. Impact: Generalized CNN using incremental learning minimizes the need for extensive training datasets to segment liver across multiple MRI sequences. With additional fine-tuning and validation, this approach can be widely applicable for sequence-independent liver segmentation and assessment of hepatic iron content.