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A fully automatic method for biological target volume segmentation of brain metastases

机译:全自动脑转移生物靶标量分割方法

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摘要

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.
机译:Leksell伽玛刀是一种微创技术,可完全破坏单个单个高剂量辐射束的脑部病变。正电子发射断层扫描(PET)成像越来越多地用于放射治疗计划。然而,由于PET图像的空间分辨率低和噪声水平高,在PET数据集中描绘病灶体积具有挑战性。如今,在PET研究中手动绘制了生物目标体积(BTV)。该过程是费时的并且取决于操作者。本文提出了一种基于图上随机游走(RW)的全自动BTV描绘算法。将结果与原始RW方法,40%阈值方法,区域增长方法和模糊c均值聚类方法的结果进行比较。为了验证所提出的方法在临床环境中的有效性,对18例脑转移患者进行了BTV分割。实验结果表明,该分割算法准确,实时,能够满足放射治疗环境中医师的要求。

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