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Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning. / Zhao, Shuo; He, Jianzhong; Li, Yongqiang и др.

Chinese Control Conference, CCC. Institute of Electrical and Electronics Engineers (IEEE), 2023. стр. 7935-7940.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Zhao, S, He, J, Li, Y, Zeng, Q, Feng, Y, Amelina, E, Amelin, M & Xu, Y 2023, Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning. в Chinese Control Conference, CCC. Institute of Electrical and Electronics Engineers (IEEE), стр. 7935-7940. https://doi.org/10.23919/ccc58697.2023.10239952

APA

Zhao, S., He, J., Li, Y., Zeng, Q., Feng, Y., Amelina, E., Amelin, M., & Xu, Y. (2023). Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning. в Chinese Control Conference, CCC (стр. 7935-7940). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.23919/ccc58697.2023.10239952

Vancouver

Zhao S, He J, Li Y, Zeng Q, Feng Y, Amelina E и др. Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning. в Chinese Control Conference, CCC. Institute of Electrical and Electronics Engineers (IEEE). 2023. стр. 7935-7940 doi: 10.23919/ccc58697.2023.10239952

Author

Zhao, Shuo ; He, Jianzhong ; Li, Yongqiang и др. / Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning. Chinese Control Conference, CCC. Institute of Electrical and Electronics Engineers (IEEE), 2023. стр. 7935-7940

BibTeX

@inproceedings{802f03149d02406e8a5b566eb5ec3dd9,
title = "Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning",
abstract = "Reconstructing the retinogeniculate visual pathway with the pituitary tumor compression is a difficult task. The tumor not only compresses the retinogeniculate visual pathway to produce a large angular deviation, but also interferes with the diffusion magnetic resonance image signal. In order to solve the above problems, the anatomical prior knowledge and fiber direction distribution are used in this study. We first manually drawn regions of interest as anatomical priors. The regions of interest are located in the eyeball and optic tract. Then, we purpose a mathematical model that combine the anatomical prior knowledge and fiber direction distribution. The mathematical model is an Markov decision process, which can be solved by reinforcement learning. In the comparison experiments, the purposed method shows high performance on retinogeniculate visual pathway reconstruction. Furthermore, only our method is able to tracking retinogeniculate visual pathway with the pituitary tumor.",
author = "Shuo Zhao and Jianzhong He and Yongqiang Li and Qingrun Zeng and Yuanjing Feng and Evgeniya Amelina and Mihail Amelin and Yile Xu",
note = "Публикация для корректировки.",
year = "2023",
doi = "10.23919/ccc58697.2023.10239952",
language = "English",
isbn = "9789887581543",
pages = "7935--7940",
booktitle = "Chinese Control Conference, CCC",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

RIS

TY - GEN

T1 - Retinogeniculate Visual Pathway Reconstruction Using Reinforcement Learning

AU - Zhao, Shuo

AU - He, Jianzhong

AU - Li, Yongqiang

AU - Zeng, Qingrun

AU - Feng, Yuanjing

AU - Amelina, Evgeniya

AU - Amelin, Mihail

AU - Xu, Yile

N1 - Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - Reconstructing the retinogeniculate visual pathway with the pituitary tumor compression is a difficult task. The tumor not only compresses the retinogeniculate visual pathway to produce a large angular deviation, but also interferes with the diffusion magnetic resonance image signal. In order to solve the above problems, the anatomical prior knowledge and fiber direction distribution are used in this study. We first manually drawn regions of interest as anatomical priors. The regions of interest are located in the eyeball and optic tract. Then, we purpose a mathematical model that combine the anatomical prior knowledge and fiber direction distribution. The mathematical model is an Markov decision process, which can be solved by reinforcement learning. In the comparison experiments, the purposed method shows high performance on retinogeniculate visual pathway reconstruction. Furthermore, only our method is able to tracking retinogeniculate visual pathway with the pituitary tumor.

AB - Reconstructing the retinogeniculate visual pathway with the pituitary tumor compression is a difficult task. The tumor not only compresses the retinogeniculate visual pathway to produce a large angular deviation, but also interferes with the diffusion magnetic resonance image signal. In order to solve the above problems, the anatomical prior knowledge and fiber direction distribution are used in this study. We first manually drawn regions of interest as anatomical priors. The regions of interest are located in the eyeball and optic tract. Then, we purpose a mathematical model that combine the anatomical prior knowledge and fiber direction distribution. The mathematical model is an Markov decision process, which can be solved by reinforcement learning. In the comparison experiments, the purposed method shows high performance on retinogeniculate visual pathway reconstruction. Furthermore, only our method is able to tracking retinogeniculate visual pathway with the pituitary tumor.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85175535839&origin=inward&txGid=cb512656a31b7096c210512ab708fbb6

UR - https://www.mendeley.com/catalogue/6f4d98f1-5a6f-35ff-975b-6df9ce51a3a8/

U2 - 10.23919/ccc58697.2023.10239952

DO - 10.23919/ccc58697.2023.10239952

M3 - Conference contribution

SN - 9789887581543

SP - 7935

EP - 7940

BT - Chinese Control Conference, CCC

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

ID: 59182395