Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography

Näppi, Janne J.; Hironaka, Tetsuo; Regge, Daniele; Yoshida, Hiroyuki

doi:10.1117/12.2217260

Cited by 18 publications

(13 citation statements)

References 23 publications

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“…Recently, deep CNNs have been applied to medical image analysis in several studies. Most of them have used deep CNNs for lesion detection or classification, while others have embedded CNNs into conventional organ‐segmentation processes to reduce the false positive rate in the segmentation results or to predict the likelihood of the image patches . Studies of this type usually divide CT images into numerous small 2D/3D patches at different locations, and then classify these patches into multiple predefined categories.…”

Section: Introductionmentioning

confidence: 99%

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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Purpose: We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. Methods: We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. Results: The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. Conclusions: We propose a single network based on pixel-to-label deep learning to address the challenging issue of anatomical structure segmentation in 3D CT cases. The novelty of this work is the policy of deep learning of the different 2D sectional appearances of 3D anatomical structures for CT cases and the majority voting of the 3D segmentation results from multiple crossed 2D sections to achieve availability and reliability with better efficiency, generality, and flexibility than conventional segmentation methods, which must be guided by human expertise.

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Section: Introductionmentioning

confidence: 99%

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

et al. 2017

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“…Identification of end-diastole and end-systole frames [30] Image segmentation DBN Left ventricle segmentation [31] CNN Knee cartilage segmentation [34] Prostate lesions segmentation [87] Image detection & segmentation CNN Localization, identification, and segmentation of vertebrae [139] Survival prediction DNN Survival prediction of amyotrophic lateral sclerosis patients [120] CT Image segmentation CNN Segmentation of liver, spleen, and kidneys [35] Kidney segmentation [36] Liver segmentation [37] Pancreas segmentation [140] Image detection CNN Colon polyp detection [141] Classification CNN…”

Section: Rnnmentioning

confidence: 99%

Clinical big data and deep learning: Applications, challenges, and future outlooks

Liu

et al. 2019

Big Data Min. Anal.

112

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The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.

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“…Therefore, an effective deep learning EC should be trained not only with tagged fecal material but also with a wide variety of examples of normal anatomy. Ideally, those examples also should be annotated into distinct categories to help in understanding the decisions made with deep learning (45).…”

Section: Limitations Of Deep Learning Ecmentioning

confidence: 99%

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

Tachibana¹,

Näppi²,

Ota³

et al. 2018

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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

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Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography

Cited by 18 publications

References 23 publications

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

Clinical big data and deep learning: Applications, challenges, and future outlooks

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

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