Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy

Mezheritsky, Tal; Romaguera, Liset Vázquez; Le, William; Kadoury, Samuel

doi:10.1016/j.media.2021.102260

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…Mezheritsky et al. developed a population-based 3D respiratory motion modeling from convolutional autoencoders for 2D ultrasound-guided radiotherapy ( 20 ). The model was trained on a variety of deformations and anatomies which enable it to generate the 3D motion experienced by the liver of a previously unseen subject.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in radiotherapy plans using deep autoencoder networks

Huang

Shang

et al. 2023

Front. Oncol.

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PurposeTreatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed.MethodsFirst, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA).ResultsThe results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively.ConclusionThe autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

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Section: Introductionmentioning

confidence: 99%

Anomaly detection in radiotherapy plans using deep autoencoder networks

Huang

Shang

et al. 2023

Front. Oncol.

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R‐MFE‐TCN: A correlation prediction model between body surface and tumor during respiratory movement

Wang,

Yang,

Liu

et al. 2024

Medical Physics

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Background2D CT image‐guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present.PurposeThe purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R‐MFE‐TCN), and to achieve accurate respiratory correlation prediction.MethodsThe model adopts a respiration‐oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual‐channel is used to retain multivariate respiratory feature, and the Multi‐headed Self‐attention obtains respiratory peak‐to‐valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz.ResultsA large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453 and 0.0361 mm, respectively, which is better than other research methods.ConclusionThe R‐MFE‐TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

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