Dufour Pascal A, Abdillahi Hannan, Ceklic Lala, Wolf-Schnurrbusch Ute, Kowal Jens
ARTORG Center for Biomedical Engineering Research, Ophthalmic Technologies, University of Bern, 3010 Bern, Switzerland.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):599-606. doi: 10.1007/978-3-642-33454-2_74.
With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.
随着采集速度和质量的提高，临床医生在日常临床实践中需要筛查的医学图像数据量开始变得具有挑战性。为了快速可视化并发现医学图像中的异常，我们提出了一种将分割算法与统计形状模型相结合的新方法。从健康人群构建的统计形状模型在健康区域会有很好的拟合。然而，该模型并不适合病变区域中经常出现的形态异常。利用统计形状模型的残差拟合误差，可以非常快速地可视化病变。这个想法被应用于在光学相干断层扫描（OCT）体积的视网膜色素上皮（RPE）中发现玻璃疣。应用了一种能够准确分割年龄相关性黄斑变性（AMD）患者玻璃疣的分割技术。然后用统计形状模型分析分割结果，以可视化潜在的病变区域。进行了广泛的评估以验证分割算法以及提示系统的质量和敏感性。检测到了大多数高度为85.5微米的玻璃疣，并且检测到了所有高度至少为93.6微米的玻璃疣。
