Ensemble of LinkNet Networks for Head and Neck Tumor Segmentation.

The segmentation of head and neck cancer (HNC) tumors is a critical step in radiotherapy treatment planning. The development of automatic segmentation algorithms has the potential to streamline the radiation oncology process. In this work, we develop an ensemble of LinkNet networks for HNC tumor segmentation as part of the HNTS-MRG 2024 Grand Challenge. A single LinkNet network, pretrained on the Imagenet dataset, was trained for 200 epochs on the HNC dataset provided by the challenge. Eight good performing weights from the internal validation set were selected to create an ensemble of 2D networks. Specifically, each selected weight was used to generate a LinkNet architecture, resulting in eight networks whose predictions were averaged to produce the final predicted segmentation. Our experiments demonstrate that the ensemble network performs better than each individual architecture, leveraging the benefits of ensemble learning without the computational cost of training each network from scratch. In the challenge's test set, the LinkNet Ensemble (team ECU) achieved an aggregated Dice score of 64.60% and 49.53% for metastatic lymph nodes and primary gross tumor segmentation, respectively, and a mean score of 57.06%.