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Segmentation of pelvic organs at risk using superpixels and graph diffusion in prostate radiotherapy

机译:在前列腺放射治疗中使用超像素和图扩散对骨盆器官进行分割

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Segmentation of organs at risk (OAR) in male pelvis is critical for planning prostate cancer radiotherapy. We are interested in femoral heads, rectum and bladder segmentation in magnetic resonance imaging (MRI) and computed tomography (CT) images in order to protect OARs during radiotherapy planning. The proposed methodology is based on superpixel algorithm in order to over-segment patient image by solving a local Eikonal function from initial seeds. Afterwards, the segmentation is obtained by computing a graph diffusion on a region adjacency graph (RAG) extracted from the over-segmentation thanks to some nodes labeled by the user. Superpixel segmentation is carried out slice-by-slice in 2D. Then, a RAG is constructed in 3D to obtain 3D OAR segmentation. The influence of the initial number of seeds on the segmentation is studied. The performances of the algorithm is evaluated and compared to 4 other methods.
机译:男性骨盆中处于危险状态的器官(OAR)的分割对于计划前列腺癌放疗至关重要。我们对磁共振成像(MRI)和计算机断层扫描(CT)图像中的股骨头,直肠和膀胱分割感兴趣,以便在放射治疗计划期间保护OAR。所提出的方法基于超像素算法,以便通过从初始种子中求解局部Eikonal函数来过度分割患者图像。之后,由于用户标记了一些节点,因此通过计算从过度分割中提取的区域邻接图(RAG)上的图扩散来获得分割。超像素分割是在2D模式下逐片进行的。然后,以3D方式构建RAG,以获得3D OAR分割。研究了种子初始数量对分割的影响。评估算法的性能并将其与其他4种方法进行比较。

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