This study attempts to segment teeth and root-canals simultaneously from CBCT
images, but there are very challenging problems in this process. First, the
clinical CBCT image data is very large (e.g., 672 *688 * 688), and the use of
downsampling operation will lose useful information about teeth and root
canals. Second, teeth and root canals are very different in morphology, and it
is difficult for a simple network to identify them precisely. In addition,
there are weak edges at the tooth, between tooth and root canal, which makes it
very difficult to segment such weak edges. To this end, we propose a
coarse-to-fine segmentation method based on inverse feature fusion transformer
and uncertainty estimation to address above challenging problems. First, we use
the downscaled volume data (e.g., 128 * 128 * 128) to conduct coarse
segmentation and map it to the original volume to obtain the area of teeth and
root canals. Then, we design a transformer with reverse feature fusion, which
can bring better segmentation effect of different morphological objects by
transferring deeper features to shallow features. Finally, we design an
auxiliary branch to calculate and refine the difficult areas in order to
improve the weak edge segmentation performance of teeth and root canals.
Through the combined tooth and root canal segmentation experiment of 157
clinical high-resolution CBCT data, it is verified that the proposed method is
superior to the existing tooth or root canal segmentation methods.

本研究针对 CBCT 图像中的牙齿和根管进行同时分割，提出了基于逆特征融合变压器和不确定性估计的粗细分割方法，通过 157 个临床高分辨率 CBCT 数据的综合实验验证该方法优于现有的牙齿或根管分割方法。