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

Bräuer, Christian; Lefere, Philippe; Gryspeerdt, Stefaan; Ringl, Helmut; Al-Mukhtar, Ali; Apfaltrer, Paul; Berzaczy, Dominik; Füger, Barbara; Furtner, Julia; Müller-Mang, C.; Pones, Matthias; Scharitzer, Martina; Woitek, Ramona; Graser, Anno; Weber, Michael; Mang, Thomas

doi:10.1007/s00330-018-5416-0

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The flexibility of our model does not restrict usage to polyps in visible light and is equally applicable for polyp detection using CT colonography. However, these newer modalities present additional challenges, such as fluid submersion obscuring polyps [ 83 ]. In fact, the scope for using the Focus U-Net architecture is not limited for colorectal polyps, and is applicable for any image segmentation problem where there is the issue of class imbalance and requirement for efficiency.…”

Section: Discussionmentioning

confidence: 99%

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

Yeung

Sala

Schönlieb

et al. 2021

Computers in Biology and Medicine

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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.

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Section: Discussionmentioning

confidence: 99%

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

Yeung

Sala

Schönlieb

et al. 2021

Computers in Biology and Medicine

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“…Based on our experience, the size of polyps ≥ 6 mm decreased by 4.1% when measured in a colon CT window (W 1500/L − 150) and by 13.4% when measured in an abdominal one (W 400/L 40) ( Fig. 10) [8]. A possible explanation for the reduction of the polyp size due to EC is the partial volume effect, which results in an attenuation gradient at the contrast-polyp interface.…”

Section: Polyp Sizementioning

confidence: 94%

“…Note that the haustral folds are preserved removed. Tagged faecal residue with a CT attenuation above the threshold of 100 HU will then be digitally extracted from the datasets by attributing the density value of air to it [8].…”

Section: Electronic Cleansingmentioning

confidence: 99%

“…In this pictorial review article, we report our observations of the clinical use of a commercially available EC algorithm (Tagged Stool Subtraction, Syngo CT Colonography VB10, Siemens Healthcare) that was applied on a database of colonoscopy-validated CTC screening-patient datasets [8], as well as in daily radiological practice. We describe the potential effects of EC on the appearance of the intraluminal anatomical morphology, as well as on the morphology and size of colonic lesions submerged under tagged residue.…”

Section: Procedures Detailsmentioning

confidence: 99%

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

et al. 2020

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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.

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Bowel Imaging

Obmann

2022

Spectral Imaging

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

Cited by 5 publications

References 28 publications

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

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

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

Bowel Imaging

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