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A novel Bayesian model incorporating deep neural network and statistical shape model for pancreas segmentation

: Ma, Jingting; Lin, Feng; Wesarg, Stefan; Erdt, Marius


Frangi, Alejandro F. (Ed.):
Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 : 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11073)
ISBN: 978-3-030-00936-6 (Print)
ISBN: 978-3-030-00937-3 (Online)
ISBN: 978-3-030-00938-0
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <21, 2018, Granada>
Fraunhofer IGD ()
Fraunhofer Singapore ()
neural network; statistical shape model (SSM); Pancreas; segmentation; Lead Topic: Individual Health; Research Area: Computer graphics (CG); Research Area: Computer vision (CV); Research Area: Modeling (MOD)

Deep neural networks have achieved significant success in medical image segmentation in recent years. However, poor contrast to surrounding tissues and high flexibility of anatomical structure of the interest object are still challenges. On the other hand, statistical shape model based approaches have demonstrated promising performance on exploiting complex shape variabilities but they are sensitive to localization and initialization. This motivates us to leverage the rich shape priors learned from statistical shape models to improve the segmentation of deep neural networks. In this work, we propose a novel Bayesian model incorporating the segmentation results from both deep neural network and statistical shape model for segmentation. In evaluation, experiments are performed on 82 CT datasets of the challenging public NIH pancreas dataset. We report 85.32 % of the mean DSC that outperforms the state-of-the-art and approximately 12 % improvement from the predicted segment of deep neural network.