Pulmonary Nodules Segmentation Method Based on Auto-encoder
Proceedings of SPIE - The International Society for Optical Engineering
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In this paper, we proposed a semi-automatic pulmonary nodule segmentation algorithm, which is operated within a region of interest for each nodule. It mainly includes two parts: the unsupervised training of auto-encoder and the supervised training of segmentation network. Applying an auto-encoder's unsupervised learning, we obtain a feature extractor that consists of its encoded part. Through adding some new neural network layers behind the feature extractor and do supervised learning on it, we get the final segmentation neural network. Compared with the traditional maximum two-dimensional entropy threshold segmentation algorithm, the dice correlation coefficient of this algorithm is 1% - 9% higher in 36 regions of interest segmentation experiments.
Auto-encoder; Feature extraction; Medical image process; Pulmonary nodule segmentation
Pulmonary Nodules Segmentation Method Based on Auto-encoder.
Proceedings of SPIE - The International Society for Optical Engineering, 10806