BACKGROUND Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency. METHODS Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice's similarity coefficient (DSC) and Sensitivity and Specificity. RESULTS Experimental study with the five patients' data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets. CONCLUSIONS Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.

中文翻译：

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基于潜在场分割的神经胶质瘤癌症多参数MRI肿瘤分割方法。

背景技术脑肿瘤的准确分割对于放射治疗中的总肿瘤体积（GTV）的定义至关重要。功能性MR图像（如表观扩散常数（ADC）和分数各向异性（FA）图像）可以为GTV的灵敏检测提供更全面的信息。我们合成了解剖和功能性MRI，以进行GTV的准确和半自动分割并提高临床效率。方法采集并登记了5例脑胶质瘤患者的4个MR图像集，包括T1加权对比增强（T1C），T2加权（T2），表观扩散常数（ADC）和分数各向异性（FA）图像。基于物理学中的势场概念，提出了一种新的势场分割方法。对于T1C，T2和ADC图像，在用户定义的感兴趣区域（ROI）上使用全局势场分割（global-PFS）进行粗略分割，然后对其进行形态学处理，以精确描绘GTV。对于FA图像，使用局部电位场分割（local-PFS）去除白质（WM），然后用区域生长和形态学方法描绘肿瘤的范围。将多参数图像的各个分割集合为一个融合分割，称为最终GTV。将GTV与手动描绘的地面真实情况进行比较，并使用分段质量度量（Q），Dice的相似系数（DSC）以及敏感性和特异性进行评估。结果对5例患者的数据和新方法进行的实验研究表明，Q，DSC，敏感性和特异性的平均值分别为0.80（±0.07），0.88（±0.04），分别为0.92（±0.01）和0.88（±0.05）。T1C，T2和ADC图像的ROI上使用的global-PFS可以避免头骨和其他非肿瘤区域的干扰。与FA图像上的本地PFS相似，与整个图像集上的global-PFS相比，它还可以减少时间复杂度。结论使用新方法可以实现GTV的高效和半自动分割。解剖和功能性MR图像的组合有可能为放射治疗中的靶标定义提供新的方法和思想。结论使用新方法可以实现GTV的高效和半自动分割。解剖和功能性MR图像的组合有可能为放射治疗中的靶标定义提供新的方法和思想。结论新方法可以实现GTV的高效和半自动分割。解剖和功能性MR图像的组合有可能为放射治疗中的靶标定义提供新的方法和思想。