Colorectal Cancer (CRC) is the third most common cancer and second most common cause of cancer deaths. Most CRCs develop from large colorectal polyps, but most polyps remain smaller than 6 mm and will never develop into cancer. Therefore, conservative selective polypectomy based on polyp size would be a much more effective colorectal screening strategy than the current practice of removing all polyps. For this purpose, automated polyp measurement would be more reproducible and perhaps more precise than manual polyp measurement in CT colonography. However, for an accurate and explainable image-based measurement, it is first necessary to determine the 3D region of the polyp. We investigated the polyp segmentation performance of a traditional 3D U-Net, transformer-based U-Net, and denoising diffusion-based U-Net on a photon-counting CT (PCCT) colonography dataset. The networks were trained on 946 polyp volumes of interest (VOIs) collected from conventional clinical CT colonography datasets, and they were tested on 17 polyp VOIs extracted from a PCCT colonography dataset of an anthropomorphic colon phantom. All three segmentation networks yielded satisfactory segmentation accuracies with average Dice scores ranging between 0.73-0.75. These preliminary results and experiences are expected to be useful in guiding the development of a deep-learning tool for reliable estimation of the polyp size for the diagnosis and management of patients in CRC screening.
We developed a novel 3D generative Artificial Intelligence (AI) method for performing Electronic Cleansing (EC) in CT Colonography (CTC). In the method, a 3D transformer based UNet is used as a generator to map an uncleansed CTC image volume directly into a virtually cleansed CTC image volume. A 3D-PatchGAN is used as a discriminator to provide feedback to the generator to improve the quality of the EC images generated by the 3D transformer-based UNet. The EC method was trained by use of the CTC image volumes of an anthropomorphic phantom that was filled partially with a mixture of foodstuff and an iodinated contrast agent. The CTC image volume of the corresponding empty phantom was used as the reference standard. The quality of the EC images was tested visually with six clinical CTC test cases and quantitatively based on a phantom test set of 100 unseen sample image volumes. The image quality of EC was compared with that of a previous 3D GAN-based EC method. Our preliminary results indicate that the 3D generative AI-based EC method outperforms our previous 3D GAN-based EC method and thus can provide an effective EC method for CTC.
We developed a novel 3D transformer-based UNet method for performing Electronic Cleansing (EC) in CT Colonography (CTC). The method is designed to map an uncleansed CTC image volume directly into the corresponding virtually cleansed CTC image volume. In the method, the layers of a 3D transformer-based encoder are connected via skip connections to the decoder layers of a 3D UNet to enhance the ability of the UNet to use long-distance image information for resolving EC image artifacts. The EC method was trained by use of the CTC image volumes of an anthropomorphic phantom that was filled partially with a mixture of foodstuff and an iodinated contrast agent. The CTC image volume of the corresponding empty phantom was used as the reference standard. The quality of the EC images was tested visually with six clinical CTC test cases and quantitatively based on a phantom test set of 100 unseen samples. The image quality of EC was compared with that of a conventional 3D UNet-based EC method. Our preliminary results indicate that the 3D transformer-based UNet EC method is a potentially effective approach for optimizing the performance of EC in CTC.
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