Effect of Treatment Using 3-Dimentional Disease Generating Model on Optical Coherence Tomography Images
Effect of Treatment Using 3-Dimentional Disease Generating Model on Optical Coherence Tomography Images
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使用三维疾病生成模型对光学相干断层扫描图像进行治疗的效果
DOI：
10.1007/978-3-319-02913-9_25
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Fumio Okuyama
中科院分区：
文献类型：
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作者：
Ngoc Anh Huyen Nguyen;Shinji Tsuruoka;Haruhiko Takase;Hiroharu Kawanaka;Hisashi Matsubara;Hisanori Yagami;Fumio Okuyama
光学相干断层扫描（OCT）图像视网膜边线的自动提取对于辅助眼科医生在诊断和治疗方面的临床决策具有重要意义。3D-OCT由128张代表横截面结构的2D-OCT图像组成。OCT在临床上已被证明对诊断多种视网膜疾病有用。一些眼科医生希望视网膜厚度的自动测量和定量评估。在此之前，已有关于视网膜OCT图像厚度自动测量方法的报道。然而，这些方法对OCT图像间歇性地提取视网膜边界线，不能自动测量异常区域的治疗效果。而且很难评估激光治疗或药物治疗的有效性。在本文中，我们提出了一个新的三维（3D）疾病生成模型来显示治疗效果。为了提取异常区域的变化，我们采用了OCT图像中视网膜厚度的自动测量方法，即本研究组提出的动态轮廓模型（One Directional Active Net， ODAN），提取了两条视网膜边界线，即内界膜（Inner limit Membrane， ILM）和视网膜色素上皮（retinal Pigment epithelial， RPE）。该生成模型基于ILM生成疾病边界线，并采用时间减法技术检测治疗效果。我们使用从正常视网膜OCT图像生成的疾病OCT图像证实了所提出模型的有效性。我们认为该方法可作为视网膜疾病治疗效果的视觉评价方法。
Automatic extraction of retinal border lines for optical coherence tomography (OCT) images is important for assisting ophthalmologists in clinical decision making in terms of both diagnosis and treatment. 3D-OCT consists of 128 2D-OCT images representing cross-sectional structure. OCT has proven clinically useful for diagnosing a variety of retinal diseases. Some ophthalmologists desire the automatic measurement of a retinal thickness and its quantitative evaluation. Previously, the automatic measurement methods of the retinal thickness have been reported for retinal OCT images. However, the methods extracted the retinal border lines intermittently for OCT images, but they cannot measure the effect of treatment for an abnormal area automatically. And it’s difficult in evaluating the effectiveness of laser treatment or medication.In this paper, we propose a new three dimensional (3D) disease generating model to display the effect of treatment. To extract the change of the abnormal area, we applied the automatic measurement method of a retinal thickness in OCT image, which is the dynamic contour model (“One Directional Active Net (ODAN)”) proposed by our research group to extract two retinal border lines, which are Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE). The proposed generating model generates the border line of disease based on ILM, and employs the temporal subtraction technique to detect the effect of treatment. We confirmed the usefulness of the proposed model using the generated disease OCT images from normal retinal OCT images. We are considering that the proposed method is useful as the visual evaluation for the effect of treatment for retinal disease.