论文阅读:Automatic Tooth Region Separation for Dental CT Images

【论文信息】
Automatic Tooth Region Separation for Dental CT Images
Hui Gao 2008 年发表的会议论文
【背景】
提出了该领域研究内容:In order to reconstruct each tooth model from images we need to handle the following problems such as separation of the upper and lower jaws, detection of the orthodontic features, fitting of the dental arch, determination of the tooth interstice, segmentation of the tooth boundary, etc.
用全景图分离牙齿在某些切片可能会出错,毕竟全景图丢失了三维信息。

本文方法还是从积分投影最小的角度出发,不过是在3D数据上面计算。沿着牙弓线每个点都计算积分投影。每个切片都拟合牙弓线,选一条最长的。(现在的方法不需要了)

【方法】
首先拟合牙弓线,然后基于牙弓线积分投影平面分离相邻牙齿。
这里写图片描述

As an AI language model, I can suggest some possible steps for automatic tooth instance segmentation and identification from cone beam CT images: 1. Pre-processing: The first step is to pre-process the CBCT images to remove noise, artifacts, and other image distortions. This can be achieved through various image processing techniques, such as filtering, thresholding, and morphological operations. 2. Tooth segmentation: The next step is to segment the individual teeth from the CBCT images. This can be done using various segmentation algorithms, such as region-growing, active contours, and watershed-based techniques. The segmentation can be further refined by incorporating shape and texture information. 3. Tooth instance identification: Once the individual teeth are segmented, the next step is to identify the specific tooth instance (e.g., mandibular left central incisor). This can be done using various methods, such as template matching, feature extraction, and machine learning-based approaches. 4. Post-processing: Finally, the segmented teeth and identified instances can be post-processed to remove any remaining artifacts or noise. This can be achieved through various techniques, such as smoothing, filtering, and morphological operations. Overall, automatic tooth instance segmentation and identification from CBCT images is a challenging task that requires the integration of various image processing, computer vision, and machine learning techniques. However, with recent advancements in AI and deep learning, there is great potential for developing accurate and reliable automated systems for dental image analysis.
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