Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography

Abstract

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. A recent trend in CT colonography is to perform a low-dose CT scanning protocol with the patient having undergone reduced-or noncathartic bowel preparation. Although several EC schemes exist, they have been developed for use with cathartic bowel preparation and highradiation-dose CT, and thus, at a low dose with noncathartic bowel preparation, they tend to generate cleansing artifacts that distract and mislead readers. Deep learning can be used for improvement of the image quality with EC at CT colonography. Deep learning EC can produce substantially fewer cleansing artifacts at dual-energy than at single-energy CT colonography, because the dual-energy information can be used to identify relevant material in the colon more precisely than is possible with the single x-ray attenuation value. Because the number of annotated training images is limited at CT colonography, transfer learning can be used for appropriate training of deep learning algorithms. The purposes of this article are to review the causes of cleansing artifacts that distract and mislead readers in conventional EC schemes, to describe the applications of deep learning and dual-energy CT colonography to EC of the colon, and to demonstrate the improvements in image quality with EC and deep learning at single-energy and dual-energy CT colonography with noncathartic bowel preparation. © RSNA, 2018 • Abbreviations: DCNN = deep convolutional neural network, EC = electronic cleansing, MFI = multimaterial feature image, 3D = three-dimensional © RSNA, 2018After completing this journal-based SA-CME activity, participants will be able to: ■ Describe the fundamentals of EC methods and the cleansing artifacts that the current EC methods generate. ■ Discuss an effective application of deep learning to virtual bowel cleansing. ■ Explain how the combined use of deep learning and dual-energy CT colonography can improve the image quality with EC. See rsna.org/learning-center-rg. SA-CME LEArning ObjECTivESThis copy is for personal use only. To order printed copies, contact reprints@rsna.org RG • Volume 38 Number 7Tachibana et al 2035

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

“…However, the potential usefulness of this software application is still limited by a number of artefacts that can affect the colonic evaluation and interpretation, as well as the size measurement of colonic lesions that are submerged under tagged faecal residue. Future strategies to further reduce EC-related pitfalls and artefacts may include EC schemes that are based on the material decomposition capability of dual-energy CT [25,26].…”

Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images

“…Those CT volumes were separated into three groups (three groups consist of 2, 2 and 3 CT volumes, respectively) for cross-validation. 6…”

“…Most intestinal segmentation methods 8,9 are applied only for the large intestines with assumption of fecal tagging by contrast agents. Virtual cleansing of feces 5,6 mentioned in the previous paragraph is also assuming fecal tagging. In summary, compared to segmentation of large intestines with fecal tagging, segmentation for ileus patients are difficult due to 1) low contrast between fluid and intestinal wall and 2) long and winding shape of the small intestines.…”

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

“…In the bowel, CT colonography computer-aided polyp detection was a hot topic in abdominal CT image analysis over a decade ago. Recent progress with deep learning has been limited but studies have reported improved electronic bowel cleansing, and higher sensitivities and lower false-positive rates for precancerous colonic polyp detection [187], [190]. Deep learning using persistent homology has recently shown success for small bowel segmentation on CT [191].…”