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Ensemble of LinkNet Networks for Head and Neck Tumor Segmentation
1Departamento de Ingeniería Industrial and Instituto de Innovación en Productividad y Logística CATENA-USFQ, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
Abstract:
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%.
Insights
An ensemble of LinkNet networks was developed for head and neck cancer (HNC) tumor segmentation, improving accuracy in radiotherapy planning. This automated approach enhances efficiency in radiation oncology workflows.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Oncology
- Radiotherapy Planning
Background:
- Accurate segmentation of head and neck cancer (HNC) tumors is crucial for effective radiotherapy treatment planning.
- Manual segmentation is time-consuming and prone to inter-observer variability.
- Automated segmentation algorithms offer a potential solution to streamline the radiation oncology process.
Purpose of the Study:
- To develop and evaluate an ensemble of LinkNet networks for automated head and neck cancer tumor segmentation.
- To participate in the HNTS-MRG 2024 Grand Challenge for HNC tumor segmentation.
- To assess the performance of ensemble learning in improving segmentation accuracy compared to individual models.
Main Methods:
- An ensemble of eight 2D LinkNet networks was created using selected weights from a single pretrained LinkNet model.
- The individual LinkNet networks were trained on the HNC dataset for 200 epochs.
- Predictions from the eight networks were averaged to generate the final segmentation mask.
Main Results:
- The LinkNet Ensemble achieved an aggregated Dice score of 64.60% for metastatic lymph nodes and 49.53% for primary gross tumors.
- The ensemble model outperformed individual LinkNet architectures.
- The overall mean score for the challenge was 57.06%.
Conclusions:
- Ensemble learning effectively enhances head and neck cancer tumor segmentation accuracy without significantly increasing computational costs.
- The developed automated segmentation method shows promise for improving radiotherapy treatment planning efficiency and precision.
- The LinkNet Ensemble demonstrated competitive performance in the HNTS-MRG 2024 Grand Challenge.
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