Chadebecq Françcois, Tilmant Christophe, Bartoli Adrien
Institut Pascal, ISIT Clermont-Ferrand, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1478-81. doi: 10.1109/EMBC.2012.6346220.
Colonoscopy is the reference medical examination for the diagnosis and treatment of neoplasia in gastroenterology. During the examination, the expert explores the colon cavity with a gastroscope in order to detect neoplasias - abnormal growths of tissue - and to diagnose which ones could be malignant. The Paris classification of superficial neoplastic lesions is the gold standard set of criteria for this type of diagnosis. One of the major criteria is the size. However, this is tremendously difficult to accurately estimate from images. This is because the absolute scale of the observed tissues is not directly conveyed in the 2D endoscopic image. We propose an image-based method to estimate the size of neoplasias. The core idea is to combine Depth-From-Focus (DFF) and Depth-From-Defocus (DFD). This allows us to recover the absolute scale by automatically detecting the blur/unblur breakpoint while the expert pulls the gastroscope away from a neoplasia. Our method is passive: it uses the image data only and thus does not require hardware modification of the gastroscope. We report promising experimental results on phantom and patient datasets.
结肠镜检查是胃肠病学中肿瘤诊断和治疗的参考医学检查。在检查过程中，专家用胃镜探查结肠腔，以检测肿瘤——组织的异常生长——并诊断哪些可能是恶性的。浅表性肿瘤病变的巴黎分类是此类诊断的金标准。其中一个主要标准是大小。然而，从图像中准确估计大小极其困难。这是因为在二维内镜图像中没有直接传达所观察组织的绝对比例。我们提出一种基于图像的方法来估计肿瘤的大小。核心思想是将聚焦深度（DFF）和离焦深度（DFD）相结合。这使我们能够在专家将胃镜从肿瘤处拉开时，通过自动检测模糊/清晰断点来恢复绝对比例。我们的方法是被动的：它仅使用图像数据，因此不需要对胃镜进行硬件修改。我们在体模和患者数据集上报告了有前景的实验结果。
