CT colonography: size reduction of submerged colorectal polyps due to electronic cleansing and CT-window settings

Optimizing Colon Identification by Window Setting Modulation on Noncontrast Computed Tomography Prior to Percutaneous Nephrolithotomy

Background: Preoperative identification of the bowel on imaging is essential in planning renal access during percutaneous nephrolithotomy (PCNL) and avoiding colonic injury. We aimed this study to assess which noncontrast computed tomography (NCCT) window setting provides the optimal colonic identification for PCNL preoperative planning. Methods: Ten urologic surgeons (four seniors, six residents) reviewed 22 images of NCCT scans in both abdomen and lung window settings in a randomized blinded order. Colonic area delineation in each image was performed using a dedicated, commercially available area calculator software. A comparison of the marked colonic area between the abdomen and lung window settings was performed. Results: Overall, the mean marked colonic area was greater in the lung window compared with the abdomen window (8.82 cm2 vs 7.4 cm2, respectively, p < 0.001). Switching the CT window from abdomen to lung increased the identified colonic area in 50 cases (50%). Intraclass correlation showed good agreement between the senior readers and among all readers (0.92 and 0.87, respectively). Similar measurements of the colonic area in both abdomen and lung windows were observed in 26/44 (60%) of the seniors cases and in 7/66 (10%) of the resident cases (p = 0.002). Conclusion: Lung window solely or in combination with abdomen window appears to provide the most accurate colonic identification for preoperative planning of PCNL access and potentially reduce the risk of colonic injury. This pattern is more evident among young urologists, and we propose to introduce it as a standard sequence in PCNL preplanning.

Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography

OBJECTIVES

To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner.

METHODS

We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better).

RESULTS

Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing.

CONCLUSIONS

Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing.

KEY POINTS

• Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

BACKGROUND

Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed.

METHOD

In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features. The Focus U-Net incorporates several further architectural modifications, including the addition of short-range skip connections and deep supervision. Furthermore, we introduce the Hybrid Focal loss, a new compound loss function based on the Focal loss and Focal Tversky loss, designed to handle class-imbalanced image segmentation. For our experiments, we selected five public datasets containing images of polyps obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, ETIS-Larib PolypDB and EndoScene test set. We first perform a series of ablation studies and then evaluate the Focus U-Net on the CVC-ClinicDB and Kvasir-SEG datasets separately, and on a combined dataset of all five public datasets. To evaluate model performance, we use the Dice similarity coefficient (DSC) and Intersection over Union (IoU) metrics.

RESULTS

Our model achieves state-of-the-art results for both CVC-ClinicDB and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of 0.809, a 14% and 15% improvement over the previous state-of-the-art results of 0.768 and 0.702, respectively.

CONCLUSIONS

This study shows the potential for deep learning to provide fast and accurate polyp segmentation results for use during colonoscopy. The Focus U-Net may be adapted for future use in newer non-invasive colorectal cancer screening and more broadly to other biomedical image segmentation tasks similarly involving class imbalance and requiring efficiency.

Electronic cleansing of tagged residue in CT colonography: what radiologists need to know

CT colonography (CTC) is the radiological examination of choice for the diagnosis of colorectal neoplasia. Faecal tagging is considered a mandatory part of bowel preparation. However, the colonic mucosa, obscured by tagged residue, is not accessible to endoluminal 3D views and requires time-consuming 2D evaluation. Electronic cleansing (EC) software algorithms can overcome this limitation by digitally subtracting tagged residue from the colonic lumen. Ideally, this enables a seamless 3D endoluminal evaluation. Despite this benefit, EC is a potential source of a wide range of artefacts. Accurate EC requires proper CTC examination technique and faecal tagging. The digital subtraction process has been shown to affect the relevant morphological features of both colonic anatomy and colonic lesions, if submerged under faecal residue. This article summarises the potential effects of EC on CTC imaging, the consequences for reporting and patient management, and strategies to avoid pitfalls. Furthermore, potentially negative effects on clinical reporting and patient management are shown, and problem-solving techniques, as well as recommendations for the appropriate use of EC techniques, are presented. Radiologists using EC should be familiar with EC-related effects on polyp size and also with correct measurement techniques.