24 March 2016 Deep learning for electronic cleansing in dual-energy CT colonography
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Abstract
The purpose of this study was to develop a novel deep-learning-based electronic cleansing (EC) method for dual-energy CT colonography (DE-CTC). In this method, an ensemble of deep convolutional neural networks (DCNNs) is used to classify each voxel of DE-CTC image volumes into one of five multi-material (MUMA) classes: luminal air, soft tissue, tagged fecal material, or a partial-volume boundary between air and tagging or that of soft tissue and tagging. Each DCNN acts as a voxel classifier. At each voxel, a region-of-interest (ROI) centered at the voxel is extracted. After mapping the pixels of the ROI to the input layer of a DCNN, a series of convolutional and max-pooling layers is used to extract features with increasing levels of abstraction. The output layer produces the probabilities at which the input voxel belongs to each of the five MUMA classes. To develop an ensemble of DCNNs, we trained multiple DCNNs based on multi-spectral image volumes derived from the DE-CTC images, including material decomposition images and virtual monochromatic images. The outputs of these DCNNs were then combined by means of a meta-classifier for precise classification of the voxels. Finally, the electronically cleansed CTC images were generated by removing regions that were classified as other than soft tissue, followed by colon surface reconstruction. Preliminary results based on 184,320 images sampled from 30 clinical CTC cases showed a higher accuracy in labeling these classes than that of our previous machine-learning methods, indicating that deep-learning-based multi-spectral EC can accurately remove residual fecal materials from CTC images without generating major EC artifacts.
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Rie Tachibana, Rie Tachibana, Janne J. Näppi, Janne J. Näppi, Toru Hironakaa, Toru Hironakaa, Se Hyung Kim, Se Hyung Kim, Hiroyuki Yoshida, Hiroyuki Yoshida, "Deep learning for electronic cleansing in dual-energy CT colonography", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851M (24 March 2016); doi: 10.1117/12.2216446; https://doi.org/10.1117/12.2216446
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