Virtual fly-over: a new visualization technique for virtual colonoscopy

An AI-Based Colonic Polyp Classifier for Colorectal Cancer Screening Using Low-Dose Abdominal CT

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%.

Virtual eversion and rotation of colon based on outer surface centerline

PURPOSE

Virtual eversion turns the colon's inner surface to its outside while maintaining the original colon path. The virtually everted colon allows both global and local views of the mucosal surface for observation. However, the conventional colon's inner surface centerline commonly used in virtual colonoscopy and virtual flattening is not suitable for virtual eversion. Therefore, the colon's outer surface centerline is introduced for virtual eversion to produce a more accurate representation.

METHODS

An improved level set segmentation method is presented for generating the colon's outer surface. To achieve eversion with fewer errors, the centerline of the colon's outer surface is employed in the proposed virtual eversion method instead of the inner surface centerline. A hybrid sampling method is designed to accelerate the eversion. Virtual rotation is introduced to visualize the lateral and rear views of the colon better. The gathered structures in the high curvature regions can be separated by virtual rotation.

RESULTS

The proposed methods were validated using two three-dimensional phantoms and 87 CT data sets. A study on the observation performance of the everted data showed that the reading times were (63% of time reduction for phantom A, 65% of time reduction for phantom B, and 77% of time reduction for CT data) less than those using virtual colonoscopy, while maintaining the sensibility. The incidence of improperly everted regions in the virtual eversion based on the outer surface centerline was 71% less than that based on the inner surface centerline.

CONCLUSIONS

The virtual eversion based on the outer surface centerline is more accurate than the one based on the inner surface centerline whether the colon's inner surface is smooth or ragged. The time required for polyp detection using the virtual eversion is considerably less than that using the conventional virtual endoscopy. Virtual eversion and virtual rotation are promising methods for the rapid location of colonic polyps. Together with virtual colonoscopy and virtual flattening, virtual eversion and virtual rotation can be integrated to produce a powerful system for diagnosing colonic lesions.

Colon unfolding via skeletal subspace deformation

We present an efficient method to digitally straighten a colon volume using mesh skinning, a technique well known in computer graphics to deform a polygonal mesh attached to a skeleton hierarchy. In our case, the colon centerline is used as the skeleton structure and the polyhedral model of the lumen as the skin that is to be deformed as the centerline is straightened. Once the colon has been straightened, we use standard rendering techniques to compute the virtual dissection. Our approach is significantly more efficient than previously proposed techniques.

[CT colonography: techniques of visualization and findings]

Evaluation and interpretation of CT colonography is based on both 2D and 3D techniques. The 2D techniques are popular mainly because the time needed for evaluation is short. The 3D techniques allow better definition than the 2D techniques, especially of polyps close to folds or at the base of a fold. The evaluation strategies generally accepted so far (primarily 2D, with 3D for problem cases, or vice versa) demand knowledge of both 2D and 3D techniques. Newer 3D visualization techniques help make it possible to acquire more complete and faster recording particularly of areas that are not easily accessible to endoscopic examination. These user-friendly developments are thus well suited to improving the detection and the security of detection of polyps. It must be remembered that experience and the knowledge of associated artifacts and the limitations they can impose on diagnosis are prime requirements for the implementation of such visualization techniques.