3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images.

Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ... Pub Date : 2017-09-01 Epub Date: 2017-09-09 DOI:10.1007/978-3-319-67564-0_4

Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu

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Abstract

Positron emission tomography - computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The "matte" values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.

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基于 PET-CT 图像上肿瘤的 3D Alpha Matting 协同分割。

正电子发射计算机断层扫描（PET-CT）已广泛应用于现代癌症成像。PET 和 CT 对肿瘤的精确划分在放射治疗中发挥着重要作用。PET-CT 协同分割技术利用了两种模式的优势，在肿瘤划分方面取得了令人瞩目的成就。在这项工作中，我们提出了一种基于三维图像消隐的新型半自动共同分割方法，用于 PET-CT 双扫描的肿瘤划分。三维图像消隐生成的 "消隐 "值被用于计算基于图的共分割的区域成本。与之前的 PET-CT 协同分割方法相比，我们的方法在设计成本函数时完全由数据驱动，因此在分割模型中使用的超参数更少。54 例肺癌患者 PET-CT 扫描的对比实验证明了我们方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, ...

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