PCA-aided fully convolutional networks for semantic segmentation of multi-channel fMRI

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2017 18th International Conference on Advanced Robotics (ICAR)
PublisherIEEE
Pages124-130
ISBN (Electronic)978-1-5386-3157-7
Publication statusPublished - Jul 2017

Conference

Title18th International Conference on Advanced Robotics (ICAR 2017)
PlaceChina
CityHong Kong
Period10 - 12 July 2017

Abstract

Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI provides more information of the pathological features. But the increased amount of data causes complexity in feature detections. This paper proposes a principal component analysis (PCA)-aided fully convolutional network to particularly deal with multi-channel fMRI. We transfer the learned weights of contemporary classification networks to the segmentation task by fine-tuning. The results of the convolutional network are compared with various methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic segmentation problem with unclear boundaries. Even with a small-sized training dataset, the test results demonstrate that our model outperforms other pathological feature detection methods. Besides, its forward inference only takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is the first time to realize pixel-wise labeling of multi-channel magnetic resonance image using FCN.

Citation Format(s)

PCA-aided fully convolutional networks for semantic segmentation of multi-channel fMRI. / Tai, Lei; Ye, Haoyang; Ye, Qiong; Liu, Ming.

Proceedings of the 2017 18th International Conference on Advanced Robotics (ICAR). IEEE, 2017. p. 124-130 8023506.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)