Name
Title | ||
|---|---|---|
Temporal Prediction of Respiratory Motion Using a Trained Ensemble of Forecasting Methods. |
Abstract | ||
|---|---|---|
Respiratory motion is a limiting factor during cancer therapy. Although image tracking can facilitate compensation for this motion, system latencies will still reduce the accuracy of tracking-based treatments. We propose a novel approach for temporal prediction of the motion of anatomical targets in the liver, observed from ultrasound sequences. The method is based on an ensemble of six prediction models, including neural networks, which are trained on motion traces and images. Using leave-one-subject-out validation on 24 liver ultrasound 2D sequences from the Challenge on Liver Ultrasound Tracking, the best performance was achieved by the linear regression-based ensemble of all methods with an accuracy of 1.49 (2.39) mm for a latency of 300 (600) ms. |
Year | Venue | Field |
|---|---|---|
2016 | MIAR | Pattern recognition,Regression,Latency (engineering),Computer science,Respiratory motion,Artificial intelligence,Image-Guided Therapy,Predictive modelling,Artificial neural network,Linear regression,Ultrasound |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoran Chen | 1 | 30 | 3.55 |
| C. Tanner | 2 | 58 | 12.36 |
| Orcun Goksel | 3 | 183 | 25.45 |
| Gábor Székely | 4 | 1697 | 193.47 |
| Valeria De Luca | 5 | 53 | 5.03 |