10:30 〜 10:45
[1F01] 3D internal points position prediction using a recurrent neural network for tumor tracking during lung cancer radiotherapy
キーワード：Optical flow, Lung cancer, Radiotherapy, Real-time recurrent learning, Recurrent neural network
In lung cancer treatment with radiotherapy, one can choose to track implanted metal markers or diaphragm points of interest, to reduce the radiation delivered to healthy tissues. But the typical treatment machine response time of approximately 500ms leads to inaccuracies in the beam delivery, which results in turn to unwanted damage to the tumor surrounding tissues. To overcome this latency, predicting the position of the tracked surrogates is necessary.
In this study we use chest 4DCBCT images of a patient breathing, with lung cancer. The image sequence is extended artificially to provide enough training and testing data. Internal points are selected and their motion during the breathing process are computed from the Lucas Kanade pyramidal iterative optical flow registration algorithm. We evaluate the performance of a recurrent neural network for the prediction of their position.
In this study we use chest 4DCBCT images of a patient breathing, with lung cancer. The image sequence is extended artificially to provide enough training and testing data. Internal points are selected and their motion during the breathing process are computed from the Lucas Kanade pyramidal iterative optical flow registration algorithm. We evaluate the performance of a recurrent neural network for the prediction of their position.