Multi-Class 3D Transformer-Based Segmentation of the Liver, Liver Vessels, and Interventional Needle on Intra-Procedural 3D MRI

Motivation: Localization of liver, liver vessels, and interventional needle on 3D magnetic resonance imaging (MRI) provides essential information for MR-guided interventions. Goal(s): To develop a multi-class network for segmenting the three classes on intra-procedural 3D MRI. Approach: 3D Swin UNEt Transformer (UNETR) with pre-trained model weights was trained with data augmentation. Needle localization was performed based on the predicted needle segmentation. Results: In six-fold cross validation of 42 3D images, the multi-class model achieved median Dice scores of 0.87, 0.64, 0.76 for liver, liver vessels and needle. The needle tip localization showed improvements compared to a single-class 3D Swin UNETR model. Impact: We trained the 3D Swin UNETR for 3D liver, liver vessel, and interventional needle segmentation on intra-procedural 3D MRI and showed that the needle localization performance can be improved using multi-class model compared to single-class model for needle localization.