Department of Bioengineering, University of California, Los Angeles, California, USA.
Department of Radiation Oncology, University of California, Los Angeles, California, USA.
Med Phys. 2022 Mar;49(3):1754-1758. doi: 10.1002/mp.15443. Epub 2022 Jan 25.
Cone-beam computed tomography (CBCT) is widely used for daily anatomy monitoring and can be a potential source to support adaptation. However, low image quality and artifacts limit CBCT's clinical utility. Peristalsis and air bubbles can cause severe artifacts in pelvic CBCT. We have observed that severe air bubble-induced Feldkamp artifacts in the rectum may contribute to low automatic segmentation accuracy.
In this study, air bubbles within the rectum were extracted and automatic rectum segmentation performance was measured in Dice similarity coefficient (DSC). A Gaussian mixture model (GMM) was used to characterize their correlation, and an expectation-maximization (EM) approach was used to solve the corresponding parameter estimation and decouple the impact from air bubbles versus other image attributes based on cluster memberships. Postprostatectomy patient data with high variability in air bubble size and shape were used in this study to reveal the regression relationship.
GMM identified two distinct correlative relations between the air-bubble severity in the rectum and the rectum prediction DSC: one showed strong negative dependency of segmentation performance on air bubble presence, and the other one had mild-to-moderate dependency that suggested another group of contributing factors influencing rectum segmentation, such as the inconsistent presence of fiducial seeds and shape extremes.
The presence of severe air bubbles contributes semilinearly to performance degradation in automatic rectum segmentation. A good correction mechanism may boost the accuracy and consistency of pelvic segmentation.
锥形束 CT(CBCT)被广泛用于日常解剖监测,并且可能成为支持自适应的潜在来源。然而,低图像质量和伪影限制了 CBCT 的临床应用。蠕动和气泡会在骨盆 CBCT 中引起严重的伪影。我们观察到,直肠中严重的气泡引起的 Feldkamp 伪影可能导致自动分割准确性降低。
在本研究中,提取了直肠内的气泡,并通过 Dice 相似系数(DSC)测量了自动直肠分割的性能。使用高斯混合模型(GMM)来描述它们的相关性,并使用期望最大化(EM)方法来解决相应的参数估计,并根据聚类成员关系将气泡的影响与其他图像属性分离。本研究使用了前列腺切除术后具有不同气泡大小和形状变化的患者数据,以揭示回归关系。
GMM 确定了直肠内气泡严重程度与直肠预测 DSC 之间存在两种不同的相关性:一种表现为分割性能与气泡存在之间的强负相关性,另一种表现为弱到中度的相关性,这表明存在另一组影响直肠分割的因素,如基准种子的不一致存在和形状极端。
严重气泡的存在会导致自动直肠分割性能的线性下降。一个良好的校正机制可能会提高骨盆分割的准确性和一致性。