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Proceedings Paper

Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography
Author(s): Rie Tachibana; Janne J. Näppi; Toru Hironaka; Se Hyung Kim; Hiroyuki Yoshida
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Paper Abstract

We developed a novel electronic cleansing (EC) method for dual-energy CT colonography (DE-CTC) based on an ensemble deep convolution neural network (DCNN) and multi-spectral multi-slice image patches. In the method, an ensemble DCNN is used to classify each voxel of a DE-CTC image volume into five classes: luminal air, soft tissue, tagged fecal materials, and partial-volume boundaries between air and tagging and those between soft tissue and tagging. Each DCNN acts as a voxel classifier, where an input image patch centered at the voxel is generated as input to the DCNNs. An image patch has three channels that are mapped from a region-of-interest containing the image plane of the voxel and the two adjacent image planes. Six different types of spectral input image datasets were derived using two dual-energy CT images, two virtual monochromatic images, and two material images. An ensemble DCNN was constructed by use of a meta-classifier that combines the output of multiple DCNNs, each of which was trained with a different type of multi-spectral image patches. The electronically cleansed CTC images were calculated by removal of regions classified as other than soft tissue, followed by a colon surface reconstruction. For pilot evaluation, 359 volumes of interest (VOIs) representing sources of subtraction artifacts observed in current EC schemes were sampled from 30 clinical CTC cases. Preliminary results showed that the ensemble DCNN can yield high accuracy in labeling of the VOIs, indicating that deep learning of multi-spectral EC with multi-slice imaging could accurately remove residual fecal materials from CTC images without generating major EC artifacts.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340E (3 March 2017); doi: 10.1117/12.2254726
Show Author Affiliations
Rie Tachibana, National Institute of Technology, Oshima College (Japan)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Janne J. Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Toru Hironaka, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Se Hyung Kim, Seoul National Univ. Hospital (Korea, Republic of)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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