| Summary: | 碩士 === 國立臺灣大學 === 電信工程學研究所 === 106 === Recently, full-field optical coherence tomography (OCT) has been developed and can get three-dimensional (3D) OCT data of human skin to achieve early diagnosis of skin cancer. In the dermatological applications of full-field OCT, dermal epidermal junction (DEJ), where melanomas and basal cell carcinomas originate, detection is an essential step for cancer diagnosis. Therefore, finding DEJ in 3D OCT data becomes an important issue for computer-aided diagnosis. However, most existing DEJ detection methods do not consider the relationship between neighboring frames. In this thesis, we proposed a novel method to find DEJ in 3D OCT data. A notable feature of our method is that it utilizes continuity of 3D data to refine the training data and train a multi-directional deep convolutional neural network (DCNN). In this way, we can eliminate noise in the training data and generate resulting DEJ with continuous surface, which follows the property of human skin. Besides, a subjective test is performed to show that the refined training data meet doctors’ standard. Finally, we evaluate our method by different metrics. The experimental results show that our method can get about 5 μm in mean error, which is significant improvement in DEJ localization.
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