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Three-Dimensional Segmentation of Hippocampus in Brain MRI Images Based on 3CN-net

Authors:

Chao Jia,

Dong Liu,

Fanshu Kong,

Changrun JiaAuthors Info & Claims

ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence

Pages 17 - 20

https://doi.org/10.1145/3319921.3319941

Published: 15 March 2019 Publication History

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Abstract

The hippocampus is very small, with complex in shape and structure, and its boundary with surrounding tissues is blurred in brain MRI images, so the traditional segmentation method cannot achieve ideal segmentation effect. In this paper, an improved deep convolution network model for biomedical imaging (3CN-net) is proposed, which can make full use of the 3d spatial information of MRI image itself, make the feature expression accurate, and reduce the neuron parameter and calculation amount; An 3d hippocampus segmentation algorithm is established, which can make full use of different levels of semantic information, improve the automatic and accurate extraction of image features, and achieve high-precision segmentation of the hippocampus in MRI images; Finally, the algorithm is compared with the experimental results of several existing optimal segmentation methods.

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Cited By

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Hassen SNeji MHussain ZHussain AAlimi AFrikha M(2024)Deep learning methods for early detection of Alzheimer’s disease using structural MR images: a surveyNeurocomputing10.1016/j.neucom.2024.127325576(127325)Online publication date: Apr-2024
https://doi.org/10.1016/j.neucom.2024.127325
Yi PJin LXu TWei LRui G(2021)Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A SurveyChinese Journal of Electronics10.1049/cje.2021.06.00230:5(793-814)Online publication date: Sep-2021
https://doi.org/10.1049/cje.2021.06.002

Index Terms

Three-Dimensional Segmentation of Hippocampus in Brain MRI Images Based on 3CN-net
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ICIAI '19: Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence

March 2019

279 pages

ISBN:9781450361286

DOI:10.1145/3319921

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
University of Texas-Dallas: University of Texas-Dallas

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Association for Computing Machinery

New York, NY, United States

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Published: 15 March 2019

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ICIAI 2019: 2019 The 3rd International Conference on Innovation in Artificial Intelligence

March 15 - 18, 2019

Suzhou, China

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Hassen SNeji MHussain ZHussain AAlimi AFrikha M(2024)Deep learning methods for early detection of Alzheimer’s disease using structural MR images: a surveyNeurocomputing10.1016/j.neucom.2024.127325576(127325)Online publication date: Apr-2024
https://doi.org/10.1016/j.neucom.2024.127325
Yi PJin LXu TWei LRui G(2021)Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A SurveyChinese Journal of Electronics10.1049/cje.2021.06.00230:5(793-814)Online publication date: Sep-2021
https://doi.org/10.1049/cje.2021.06.002

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Combining Residual learning and U-Net for Hippocampus Segmentation of Brain MRI Volume Image

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters

Multiscale Segmentation of Three-Dimensional MR Brain Images

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