The quantity and variety of CT imaging data are essential components for effective AI-model training. However, the availability of high-quality CT images for organ segmentation is quite constrained, and the AI-based organ segmentation could be impacted by the varying intensity of contrast agents. Therefore, to improve the robustness of the segmentation both with and without a contrast agent, as well as to solve the data shortage issue, we proposed a multi-planar UNet with an augmented contrast-boosting technique. Any program employing the proposed method may see greater benefits from reducing the burden of large-scale dataset preparation, improving the AI-model training efficiency.
Three dimensional CT images have enabled dentists to visualize the overall anatomic structure of teeth and jawbones. Dental cone-beam CT(CBCT) images include a lot more information as compared to panoramic radiographs, but it is hard to assess overall teeth and jaw structures at a single glance. Although panoramic images facilitate the evaluation process of overall anatomic structures, they may include geometric distortions, blurs, and superimposition of multiple structures due to the spine and ghost effects. Some image viewers have a cut-viewing function that enables orthogonal viewing along the user-set dental arch. However, the orthogonal viewing looks different from the panoramic image physically taken by a panoramic scanner. To make more convenient use of CBCT images, we have developed an approach to synthesize panoramic images from the CBCT images. In the synthesis of the panoramic images, we removed the ghost and spine superimposition to improve the visibility of the synthesized panoramic images.
The panoramic image synthesis has been done in three steps. At first, we extracted the panoramic dental arch from the CBCT images. At the next step, we removed the ghost-inducing bone parts from the CBCT images. Lastly, we synthesized panoramic images by stitching thousands of partial-view panoramic projection images.
With the advantage of the synthesized panoramic image, the additional panoramic scan is not further necessary, and undesirable features of conventional panoramic radiographs (e.g., ghost artifacts or cervical spine superimposition) can be entirely removed.
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