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This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.
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참고문헌 (32)
참고문헌 신청
E. Shelhamer / 2017 / Fully Convolutional Networks for Semantic Segmentation / IEEE Trans. Pattern Anal. Mach. Intell 39(4):640~651
O. Ronneberger / 2015 / U-net: Convolutional networks for biomedical image segmentation / Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9351:234~241
T. Y. Lin / 2017 / Feature pyramid networks for object detection / Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 2017:936~944
김서정 / 2020 / Multi-Tasking U-net 기반 파프리카 병해충 진단 / 스마트미디어저널 9(1):16~22
M. Längkvist / 2014 / Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning / Pattern Recognit. Lett 42(1):11~24
O. Ronneberger / 2015 / U-net: Convolutional networks for biomedical image segmentation / Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9351:234~241
T. Y. Lin / 2017 / Feature pyramid networks for object detection / Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 2017:936~944
김서정 / 2020 / Multi-Tasking U-net 기반 파프리카 병해충 진단 / 스마트미디어저널 9(1):16~22
M. Längkvist / 2014 / Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning / Pattern Recognit. Lett 42(1):11~24
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