Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy Int.

Tai A Liang Z Erickson B Li X Management of respiration-induced motion with 4-dimensional computed tomography (4DCT) for pancreas irradiation Int.

The use of gated and 4D CT imaging in planning for stereotactic body radiation therapy Med. The experimental results demonstrated that the proposed method could attain an accurate DIR between any two 4D-CT phases within one minute. The network was trained and tested on 15 patients’ 4D-CT abdominal datasets using five-fold out cross validation. A dilated inception module (DIM) was integrated into the network to extract multi-scale motion features for robust feature learning.

The proposed network was trained in an unsupervised fashion without the need of ground truth DVF or anatomical labels. In this paper, we present our preliminary results on using a fast-unsupervised generative adversarial network (GAN) to generate deformation vector fields (DVF) for 4D-CT DIR to help motion management and treatment planning in radiation therapy. However, traditional DIR methods such as optical flow and demons are iterative and generally slow especially for large 4D-CT datasets. Accurate and fast deformable image registration (DIR) on 4D-CT could aid the treatment planning process in target definition, tumor tracking, organ-at-risk (OAR) sparing, and respiratory gating. Respiration-induced abdominal tissue motion causes significant problems in effective irradiation of abdominal cancer patients. It can provide valuable information on the shapes and trajectories of tumor and normal structures to guide treatment planning and improve the accuracy of tumor delineation. Four-dimensional computed tomography (4D-CT) has been used in radiation therapy to allow for tumor and organ motion tracking throughout the breathing cycle.

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