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Automated Liver Segmentation Method for CBCT Dataset by Combining Sparse Shape Composition and Probabilistic Atlas Construction

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D Li

Dengwang Li1*, Li Liu2 , Jinhu Chen3 , Hongsheng Li4 , (1) Shandong Normal University, Jinan, shandong province, (2) Shandong Normal University, Jinan, shandong, (3) Shandong Cancer Hospital&Institute, Jinan, Shandong, (4) Shandong Cancer Hospital and Institute, Jinan, shandong


SU-E-I-87 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

The aiming of this study was to extract liver structures for daily Cone beam CT (CBCT) images automatically.
Datasets were collected from 50 intravenous contrast planning CT images, which were regarded as training dataset for probabilistic atlas and shape prior model construction. Firstly, probabilistic atlas and shape prior model based on sparse shape composition (SSC) were constructed by iterative deformable registration. Secondly, the artifacts and noise were removed from the daily CBCT image by an edge-preserving filtering using total variation with L1 norm (TV-L1). Furthermore, the initial liver region was obtained by registering the incoming CBCT image with the atlas utilizing edge-preserving deformable registration with multi-scale strategy, and then the initial liver region was converted to surface meshing which was registered with the shape model where the major variation of specific patient was modeled by sparse vectors. At the last stage, the shape and intensity information were incorporated into joint probabilistic model, and finally the liver structure was extracted by maximum a posteriori segmentation.
Regarding the construction process, firstly the manually segmented contours were converted into meshes, and then arbitrary patient data was chosen as reference image to register with the rest of training datasets by deformable registration algorithm for constructing probabilistic atlas and prior shape model. To improve the efficiency of proposed method, the initial probabilistic atlas was used as reference image to register with other patient data for iterative construction for removing bias caused by arbitrary selection.
The experiment validated the accuracy of the segmentation results quantitatively by comparing with the manually ones. The volumetric overlap percentage between the automatically generated liver contours and the ground truth were on an average 88%-95% for CBCT images.
The experiment demonstrated that liver structures of CBCT with artifacts can be extracted accurately for following adaptive radiation therapy.

Funding Support, Disclosures, and Conflict of Interest: This work is supported by National Natural Science Foundation of China (No. 61201441), Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (No. BS2012DX038), Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN23), Jinan youth science and technology star (No.20120109 )

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