PCA支援によるマルチチャネルfMRIの意味的セグメンテーション向け全畳み込みネットワーク
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
PCAで次元圧縮したマルチチャネルfMRIに対し、転移学習した全畳み込みネットワークで画素単位の意味的セグメンテーションを行う手法を提案し、境界が不明瞭な病変のラベリングを高精度・高速に実現した。
著者: Lei Tai, Haoyang Ye, Qiong Ye, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
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.
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