Version 1
: Received: 14 June 2023 / Approved: 15 June 2023 / Online: 15 June 2023 (07:57:43 CEST)
How to cite:
Lee, K.H.; Kang, Y.N. Evaluation of Deformable Image Registration Algorithms in Computed Tomography of Head and Neck Cancer: Preliminary Study. Preprints2023, 2023061099. https://doi.org/10.20944/preprints202306.1099.v1
Lee, K.H.; Kang, Y.N. Evaluation of Deformable Image Registration Algorithms in Computed Tomography of Head and Neck Cancer: Preliminary Study. Preprints 2023, 2023061099. https://doi.org/10.20944/preprints202306.1099.v1
Lee, K.H.; Kang, Y.N. Evaluation of Deformable Image Registration Algorithms in Computed Tomography of Head and Neck Cancer: Preliminary Study. Preprints2023, 2023061099. https://doi.org/10.20944/preprints202306.1099.v1
APA Style
Lee, K.H., & Kang, Y.N. (2023). Evaluation of Deformable Image Registration Algorithms in Computed Tomography of Head and Neck Cancer: Preliminary Study. Preprints. https://doi.org/10.20944/preprints202306.1099.v1
Chicago/Turabian Style
Lee, K.H. and Young Nam Kang. 2023 "Evaluation of Deformable Image Registration Algorithms in Computed Tomography of Head and Neck Cancer: Preliminary Study" Preprints. https://doi.org/10.20944/preprints202306.1099.v1
Abstract
.Purpose: This paper presents a comparative analysis of state-of-the-art DIR algorithms in the context of head and neck squamous cell carcinoma (HNSCC) radiation therapy.
Materials and Methods: We used a dataset of head and neck cancer patients within the Cancer Image Archive, which includes 31 patients with 3D CT image data from pre-treatment, mid-treatment, and post-treatment stages of radiotherapy. We applied DIR algorithms to two datasets, one for pre-treatment to mid-treatment registration and another for mid-treatment to post-treatment registration, by aligning one CT image to another CT image. To quantitatively analyze the patient data, we focused on the spinal cord, optic nerve, brain stem, cochlea, and PTV as the OAR datasets. To verify the DIR algorithm, we used two non-learning-based methods, SyN and NiftyReg, as well as three deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph. We trained a deep learning model by adjusting the ratio of the training dataset, validation dataset, and test dataset to 7:1:2.
Results: The average DSCs of SyN and NiftyReg, the non-learning-based methods, were 0.74±0.06 and 0.70±0.12, respectively. The deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph, had average DSCs of 0.72±0.08, 0.68±0.11, and 0.69±0.13, respectively. The deep learning DIR algorithm produced transformed outputs of OAR/PTV in a shorter time than other models, including commercial and conventional mathematical algorithms (At Inference(sec/images), Voxelmorph: 0.36, Cyclemorph: 0.3, Transmorph: 5.1, SyN: 140, NiftyReg: 40.2).
Conclusions: In conclusion, our study provides a comprehensive comparison of commercial DIR algorithms with traditional and deep learning-based DIR methods. Our results demonstrate that commercial DIR algorithms outperform traditional DIR methods in terms of accuracy and efficiency. However, we also show that deep learning-based DIR methods have the potential to achieve similar performance to commercial algorithms with proper training and optimization. Our findings suggest that both commercial and deep learning-based DIR methods have their respective advantages and limitations, and the choice of method should depend on the specific requirements of the clinical application. Overall, our study contributes to ongoing efforts to improve the accuracy and efficiency of DIR methods for better patient outcomes.
Keywords
Deformable Image Registration; Head and neck Squamous cell carcinoma; Computed tomography
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
