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Segmentation of Blood Clot from CT Pulmonary Angiographic Images Using a Modified Seeded Region Growing Algorithm Method

机译:改进的种子区生长算法从CT肺血管造影图像中分割血块

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Pulmonary embolism (PE) is a medical condition defined as the obstruction of pulmonary arteries by a blood clot, usually originating in the deep veins of the lower limbs. PE is a common but elusive illness that can cause significant disability and death if not promptly diagnosed and effectively treated. CT Pulmonary Angiography (CTPA) is the first line imaging study for the diagnosis of PE. While clinical prediction rules have been recently developed to associate short-term risks and stratify patients with acute PE, there is a dearth of objective biomarkers associated with the long-term prognosis of the disease. Clot (embolus) burden is a promising biomarker for the prognosis and recurrence of PE and can be quantified from CTPA images. However, to our knowledge, no study has reported a method for segmentation and measurement of clot from CTPA images. Thus, the purpose of this study was to develop a semi-automated method for segmentation and measurement of clot from CTPA images. Our method was based on Modified Seeded Region Growing (MSRG) algorithm which consisted of two steps: (1) the observer identifies a clot of interest on CTPA images and places a spherical seed over the clot; and (2) a region grows around the seed on the basis of a rolling-ball process that clusters the neighboring voxels whose CT attenuation values are within the range of the mean ± two standard deviations of the initial seed voxels. The rolling-ball propagates iteratively until the clot is completely clustered and segmented. Our experimental results revealed that the performance of the MSRG was superior to that of the conventional SRG for segmenting clots, as evidenced by reduced degrees of over- or under-segmentation from adjacent anatomical structures. To assess the clinical value of clot burden for the prognosis of PE, we are currently applying the MSRG for the segmentation and volume measurement of clots from CTPA images that are acquired in a large cohort of patients with PE in an on-going NIH-sponsored clinical trial.
机译:肺栓塞(PE)是一种医学病症,定义为通常由下肢深静脉引起的血块阻塞肺动脉。 PE是一种常见但难以捉摸的疾病,如果不及时诊断和有效治疗,会导致严重的残疾和死亡。 CT肺血管造影(CTPA)是诊断PE的一线影像学研究。尽管最近已经开发了临床预测规则来关联短期风险和对急性PE患者进行分层,但缺乏与疾病长期预后相关的客观生物标志物。凝块(栓子)负担是PE的预后和复发的有前途的生物标志物,可以从CTPA图像中量化。然而,据我们所知,尚无研究报道从CTPA图像中分割和测量血块的方法。因此,本研究的目的是开发一种半自动方法,用于从CTPA图像中分割和测量血块。我们的方法基于改进的种子区域生长(MSRG)算法,该算法包括两个步骤:(1)观察者在CTPA图像上识别出感兴趣的血块并将球形种子放在血块上; (2)基于滚球过程,在种子周围生长一个区域,该区域将CT衰减值在初始种子体素的平均值±两个标准偏差范围内的相邻体素聚类。滚球不断地传播,直到血块完全聚集和分割为止。我们的实验结果表明,在分割血块方面,MSRG的性能优于传统SRG的性能,这可以从相邻解剖结构的过度分割或分割不足的程度得到证明。为了评估血块负担对PE预后的临床价值,我们目前正在将MSRG用于CTPA图像的血块分割和体积测量,这些图像是在持续的NIH资助下从大量PE患者中获取的CTPA图像临床试验。

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