Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA.
Kentucky Imaging Technologies, LLC, Louisville, KY 40245, USA.
Sensors (Basel). 2022 Dec 13;22(24):9761. doi: 10.3390/s22249761.
Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides "filet"-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.
在非侵入性结直肠癌（CRC）筛查方法中，计算机断层扫描结肠成像（CTC）和虚拟结肠镜检查（VC）的准确性更高。本工作提出了一种基于人工智能的虚拟结肠镜检查（VC）中息肉检测框架。本工作主要解决两个步骤：从背景中自动分割出结肠区域，以及自动检测息肉。此外，我们还在低剂量计算机断层扫描（CT）扫描上评估了所提出框架的性能。我们基于我们的可视化方法 Fly-In（FI），它提供了结肠内部表面的“鱼片”状投影。FI 方法的性能证实了它帮助胃肠病学家的能力，并且在对抗 CRC 方面具有很大的潜力。在这项工作中，将这些 FI 的 2D 投影与 3D 结肠表示融合，以生成新的合成图像。使用合成图像来训练 RetinaNet 模型以检测息肉。训练好的模型具有 94%的 f1 分数和 97%的灵敏度。此外，我们研究了 CT 扫描剂量变化对 FI 方法在息肉可视化中的性能的影响。使用 FI 为 CTC 可视化开发了一个模拟平台，用于常规 CTC 和低剂量 CTC。这是通过使用一种新颖的人工智能恢复算法来实现的，该算法增强了低剂量 CT 图像，以便可以使用 FI 方法成功重建和可视化 3D 结肠。三位资深的认证放射科医生评估了该框架在 30kV 峰值电压下的性能，该平台的平均相对灵敏度为 92%，而 60kV 峰值电压的平均相对灵敏度为 99.5%。
