Related Experiment Video
Updated: Aug 4, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel
Lin-Mei Zhao1,2,3, Helen Zhang4, Daniel D Kim4
1National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan, 410008, People's Republic of China.
Abstract:
Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this study is to develop a novel deep learning segmentation model for H&N cancer based on independent and combined CT and FDG-PET modalities.Approach. In this study, we developed a robust deep learning-based model leveraging information from both CT and PET. We implemented a 3D U-Net architecture with 5 levels of encoding and decoding, computing model loss through deep supervision. We used a channel dropout technique to emulate different combinations of input modalities. This technique prevents potential performance issues when only one modality is available, increasing model robustness. We implemented ensemble modeling by combining two types of convolutions with differing receptive fields, conventional and dilated, to improve capture of both fine details and global information.Main Results. Our proposed methods yielded promising results, with a Dice similarity coefficient (DSC) of 0.802 when deployed on combined CT and PET, DSC of 0.610 when deployed on CT, and DSC of 0.750 when deployed on PET.Significance. Application of a channel dropout method allowed for a single model to achieve high performance when deployed on either single modality images (CT or PET) or combined modality images (CT and PET). The presented segmentation techniques are clinically relevant to applications where images from a certain modality might not always be available.
Insights
A novel deep learning model accurately segments head and neck (H&N) cancer tumors using CT and PET scans. This automated method improves therapeutic management by ensuring precise gross tumor volume segmentation, even with single imaging modalities.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Oncology
Background:
- Accurate gross tumor volume segmentation is crucial for effective head and neck (H&N) cancer radiation therapy.
- Current segmentation methods may lack robustness when dealing with single imaging modalities.
Purpose of the Study:
- To develop a novel deep learning segmentation model for H&N cancer.
- To leverage combined CT and FDG-PET imaging data for improved segmentation accuracy.
Main Methods:
- A 3D U-Net architecture with deep supervision was implemented.
- Channel dropout was used to ensure model robustness with single or combined imaging modalities.
- Ensemble modeling combined conventional and dilated convolutions for enhanced feature capture.
Main Results:
- The model achieved a Dice Similarity Coefficient (DSC) of 0.802 for combined CT and PET.
- Performance was also strong on single modalities: DSC of 0.610 for CT and 0.750 for PET.
- The channel dropout technique enabled high performance across all tested modality combinations.
Conclusions:
- The developed deep learning model offers a robust and accurate solution for H&N cancer gross tumor volume segmentation.
- The model's adaptability to single or combined imaging modalities enhances its clinical relevance.
- This approach supports therapeutic management in scenarios where specific imaging data may be unavailable.
