Evaluation of multi-modal image segmentation for radiotherapy planning

The Global Cancer Observatory estimates approximately 900,000 head and neck cancer cases annually. Accurate segmentation of tumors and lymph nodes from medical images is essential for cancer treatment planning. Manual segmentation by experts slows the clinical workflow and is subject to inter-observer variability. Deep learning-based segmentation methods can address these issues, improving efficiency and accuracy. The study compares Squeeze-and-Excitation (SE) U-Net and SegResNet architectures for multi-class tumor segmentation. They were trained and tested with the HECKTOR 2022 dataset to evaluate model architecture and hyperparameters in both efficiency and accuracy. The models delineated gross primary tumors and lymph node tumors in the head and neck region, with a ground truth manual segmentation. Trained on NVIDIA Tesla V100-PCIE GPU with 32GB memory, up to eight core CPU with 16GB memory each, the models had mean DSC of 0.79 and 0.61 for SegResNet and SE U-Net, respectively over 300 epochs for a test set of 100 image pairs. Gross tumor accuracy was 0.73 and 0.65, respectively. Nodal tumor accuracy was lower with DSC of 0.70 and 0.45, respectively. The SegResNet model performed comparably with grand challenge submissions, likely due to more layers and filters. The SegResNet model was more scalable to higher resolutions and had a higher computational efficiency. Deep learning model architectures show promising performance that could be considered for integration into clinical workflows for multi-tumor segmentation in soft tissues.