Automated Polyp Measurement with CT Colonography: Preliminary Observations in a Phantom Colon Model

Fletcher, Joel G.; Booya, Fargol; Melton, Zachary John; Johnson, Kristina T.; Guendel, Lutz; Schmidt, Bernhard; McCollough, Cynthia H.; Young, Brett M.; Fidler, Jeff L.; Harmsen, William S.

doi:10.2214/ajr.06.1169

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…In a glass colon model it was found that, by using a commercial automated polyp measurement tool, accuracy of polyp size measurements depended on polyp size, morphology, and location with respect to the tip of haustral folds ( 39 ). Size measurements were less accurate for 5-mm polyps, fl at polyps, and polyps on haustral fold tips.…”

Section: Automated Measurementsmentioning

confidence: 99%

Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?

Summers¹

2010

Radiology

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Section: Automated Measurementsmentioning

confidence: 99%

Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?

Summers¹

2010

Radiology

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“…A number of investigators have developed methods of automated polyp size measurement [14–18]. These methods assessed polyp width or a combination of width and height, but not height alone, since the focus was to obtain a measurement comparable to the size measurements determined at optical colonoscopy.…”

Section: Discussionmentioning

confidence: 99%

Automated Measurement of Colorectal Polyp Height at CT Colonography: Hyperplastic Polyps Are Flatter Than Adenomatous Polyps

Summers

Liu

Yao

et al. 2009

American Journal of Roentgenology

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Objective It is known that hyperplastic polyps are more difficult to detect than adenomatous polyps at CT colonography (CTC) and it has been theorized that this is because hyperplastic polyps are flatter. Using automated software that computes polyp height, we determined whether hyperplastic colonic polyps on CTC are indeed flatter than adenomatous polyps of comparable width. Materials and Methods 1186 screening patients at 3 medical centers underwent oral contrast-enhanced CTC and same-day optical colonoscopy (OC) with segmental unblinding. 185 of the patients had at least one hyperplastic or adenomatous polyp 6–10 mm in size visible at CTC, where size was determined by a calibrated guidewire at OC. To assess flatness, the heights of the polyps at CTC were measured using a validated automated software program. Heights and height-to-width ratios of the hyperplastic polyps were compared to those of the adenomatous polyps using a t-test (two-tailed, unpaired, unequal variance). Results There were 176 adenomatous and 83 hyperplastic polyps visible at segmentally unblinded OC. The fraction of these polyps that were measurable at CTC using the automated software was not significantly different for adenomatous versus hyperplastic polyps (158/176, 89.8% versus 73/83, 83.9%, p=0.2). The average height-to-width ratios using automated width measurements were 15% less for hyperplastic polyps: 0.39±0.20 (n=158) and 0.33±0.19 (n=73) for adenomatous and hyperplastic polyps, respectively (p=0.03). When polyps of comparable OC size or CTC width were considered, the heights of hyperplastic polyps were up to 27% less than those of adenomatous polyps. Conclusions For 6–10 mm polyps of a given size as determined by optical colonoscopy or a given width at CT colonography, hyperplastic polyps tend to be flatter (i.e., have lower height) compared to adenomatous polyps.

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“…9 Measurement errors have been observed in particular for polyps that are flat or irregularly shaped, or that are located on tips of haustral folds. 10 Before measuring the polyp size, it is first useful to determine the extent of the 3D region covered by the polyp. U-Net architecture 11 is one of the most well-known deep-learning approaches for performing segmentation on medical images.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography

Näppi,

Hironaka,

et al. 2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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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.

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Automated Polyp Measurement with CT Colonography: Preliminary Observations in a Phantom Colon Model

Cited by 10 publications

References 18 publications

Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?

Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?

Automated Measurement of Colorectal Polyp Height at CT Colonography: Hyperplastic Polyps Are Flatter Than Adenomatous Polyps

Automated segmentation of polyps by 3D deep learning in photon-counting CT colonography

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