Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta GA, 30332, USA; Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, PA, Italy; Department of Industrial and Digital Innovation (DIID) - University of Palermo, PA, Italy.
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, PA, Italy.
Artif Intell Med. 2019 Mar;94:67-78. doi: 10.1016/j.artmed.2019.01.002. Epub 2019 Jan 8.
In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.
在使用正电子发射断层扫描数据集进行癌症描绘的背景下，我们提出了一种创新方法，旨在以完全或至少几乎完全自动化的方式实时进行三维分割任务。该方法包括初步初始化阶段，在此阶段用户只需在体数据集的一个切片上突出显示癌症周围的感兴趣区域。该算法负责识别异常组织周围的最佳且与用户无关的感兴趣区域，并位于包含最高标准化摄取值的切片上，以便开始连续的分割任务。然后使用逐层逼近方法重建三维体积，直到满足合适的自动停止条件。在每一层，使用基于最小化新能量函数的增强局部主动轮廓进行分割，该能量函数结合了机器学习组件（在本研究中为判别分析）提供的信息。结果，整个算法几乎完全自动化，输出分割与用户提供的输入无关。使用球体和沸石的体模实验，以及包含各种身体部位（肺、脑和头颈部）和两种不同示踪剂（18F-氟代-2-脱氧-D-葡萄糖和 11C 标记蛋氨酸）的临床病例来评估算法性能。球体和沸石的体模实验的骰子相似系数分别高于 90%和 80%。临床病例与金标准高度一致（R=0.98）。这些结果表明，该方法可以有效地应用于临床常规，在治疗反应评估和放射治疗靶向方面具有潜在益处。
