在牙科治療領域中，對牙根形態的精確認識扮演著關鍵角色。特別是對於上顎第 二大臼齒，由於其位置的特殊性以及形態的變異性，牙周病治療、假牙製作及根管治 療等方面的難度顯著增加。如果能在手術前預先瞭解其牙根融合和解剖結構的變異， 將大大促進治療過程的順利進行並提高成功率。面對此類複雜病例時，臨床醫師常借 助錐束狀電腦斷層掃描(CBCT)來獲取精確的牙齒結構資訊，此技術在根管結構研究中 也被廣泛運用。近年來，計算機輔助診斷領域的發展使得訓練有素的人工智慧(AI)在臨 床診斷中提供了更高的準確度。然而，在牙科領域，有關計算機輔助診斷的研究仍相 對不足。本文旨在收集 CBCT 影像，以此進行計算機輔助診斷的人工智慧訓練，半自 動偵測病患的牙根融合形態特徵。我們將開發出一套系統，協助臨床醫師更加精準地 解讀 CBCT 中的牙根資訊。

In the field of dental treatment, accurate understanding of root morphology plays a crucial role. This is especially true for the maxillary second molar, where the complexity is significantly increased due to its unique position and morphological variations. If the variations in root fusion and anatomical structure can be understood prior to surgery, it will greatly facilitate the treatment process and improve success rates. For such complex cases, clinicians often rely on Cone-Beam Computed Tomography (CBCT) to obtain precise information about the tooth structure, a technique widely used in endodontic research. In recent years, the development of Computer-Aided detection has enabled well-trained Artificial Intelligence (AI) to provide higher accuracy in clinical diagnosis. However, research on computer-aided detection in the field of dentistry remains relatively insufficient. This study aims to collect CBCT images for AI training in computer-aided detection to semi-automatically detect the root fusion morphological characteristics of patients. We will develop a system to assist clinicians in more accurately interpreting root information in CBCT scans.

摘要...i
Abstract...ii
誌謝...iii
目錄...iv
表目錄...v
圖目錄...vi
第一章 介紹...1
1.1 動機...1
1.2 上顎第二大臼齒及牙根融合...1
1.3 計算機輔助診斷...3
第二章 實驗方法...4
2.1 CBCT影像數據集...4
2.2 牙冠切片的人工挑選與標記...6
2.3 牙根形態自動辨識...8
2.4 自動辨識牙根形態...10
2.5 統計與分析...11
第三章 實驗結果與分析...12
3.1 牙冠切片影像分割...12
3.2 立體牙齒分割...13
3.3 牙根範圍篩選...14
3.4 自動辨識牙根形態...15
文獻參考...16
Extended Abstract...19

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