Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network

Jingxin Liu; Libo Liu; Bolei Xu; Xianxu Hou; Bozhi Liu; Xin Chen; Linlin Shen; Guoping Qiu

doi:10.1109/ISBI.2019.8759422

Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network

Jingxin Liu, Libo Liu, Bolei Xu, Xianxu Hou, Bozhi Liu, Xin Chen, Linlin Shen, Guoping Qiu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

14 Citations (Scopus)

Abstract

Recognition and segmentation of bladder walls and tumour in MRI is essential for bladder cancer diagnosis. In this paper, we propose a novel Pyramid in Pyramid (PiP) fully convolutional neural network to address this problem. A pyramid backbone with lateral connections between encoder and decoder is utilized to segment the bladder wall and tumour at multiple scales and in an end-to-end fashion. To boost the model's capability of extracting multiscale contextual information, a pyramidal atrous convolution block is embedded into the pyramid backbone. We present experimental results to show that the new method outperforms other state-of-the-art models and that the results have a good consistency with that of experienced radiologists.

Original language	English
Title of host publication	ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
Pages	28-31
Number of pages	4
ISBN (Electronic)	9781538636411
DOIs	https://doi.org/10.1109/ISBI.2019.8759422
Publication status	Published - Apr 2019
Externally published	Yes
Event	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: 8 Apr 2019 → 11 Apr 2019

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2019-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/Territory	Italy
City	Venice
Period	8/04/19 → 11/04/19

Keywords

Bladder cancer
Deep learning
MRI
Segmentation

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISBI.2019.8759422

Cite this

Liu, J., Liu, L., Xu, B., Hou, X., Liu, B., Chen, X., Shen, L., & Qiu, G. (2019). Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 28-31). Article 8759422 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759422

@inproceedings{4c6628206ec64f01be5db56a25128cd0,

title = "Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network",

abstract = "Recognition and segmentation of bladder walls and tumour in MRI is essential for bladder cancer diagnosis. In this paper, we propose a novel Pyramid in Pyramid (PiP) fully convolutional neural network to address this problem. A pyramid backbone with lateral connections between encoder and decoder is utilized to segment the bladder wall and tumour at multiple scales and in an end-to-end fashion. To boost the model's capability of extracting multiscale contextual information, a pyramidal atrous convolution block is embedded into the pyramid backbone. We present experimental results to show that the new method outperforms other state-of-the-art models and that the results have a good consistency with that of experienced radiologists.",

keywords = "Bladder cancer, Deep learning, MRI, Segmentation",

author = "Jingxin Liu and Libo Liu and Bolei Xu and Xianxu Hou and Bozhi Liu and Xin Chen and Linlin Shen and Guoping Qiu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

year = "2019",

month = apr,

doi = "10.1109/ISBI.2019.8759422",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "28--31",

booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",

address = "United States",

}

Liu, J, Liu, L, Xu, B, Hou, X, Liu, B, Chen, X, Shen, L & Qiu, G 2019, Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759422, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, IEEE Computer Society, pp. 28-31, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, 8/04/19. https://doi.org/10.1109/ISBI.2019.8759422

Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network. / Liu, Jingxin; Liu, Libo; Xu, Bolei et al.
ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. p. 28-31 8759422 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network

AU - Liu, Jingxin

AU - Liu, Libo

AU - Xu, Bolei

AU - Hou, Xianxu

AU - Liu, Bozhi

AU - Chen, Xin

AU - Shen, Linlin

AU - Qiu, Guoping

PY - 2019/4

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N2 - Recognition and segmentation of bladder walls and tumour in MRI is essential for bladder cancer diagnosis. In this paper, we propose a novel Pyramid in Pyramid (PiP) fully convolutional neural network to address this problem. A pyramid backbone with lateral connections between encoder and decoder is utilized to segment the bladder wall and tumour at multiple scales and in an end-to-end fashion. To boost the model's capability of extracting multiscale contextual information, a pyramidal atrous convolution block is embedded into the pyramid backbone. We present experimental results to show that the new method outperforms other state-of-the-art models and that the results have a good consistency with that of experienced radiologists.

AB - Recognition and segmentation of bladder walls and tumour in MRI is essential for bladder cancer diagnosis. In this paper, we propose a novel Pyramid in Pyramid (PiP) fully convolutional neural network to address this problem. A pyramid backbone with lateral connections between encoder and decoder is utilized to segment the bladder wall and tumour at multiple scales and in an end-to-end fashion. To boost the model's capability of extracting multiscale contextual information, a pyramidal atrous convolution block is embedded into the pyramid backbone. We present experimental results to show that the new method outperforms other state-of-the-art models and that the results have a good consistency with that of experienced radiologists.

KW - Bladder cancer

KW - Deep learning

KW - MRI

KW - Segmentation

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U2 - 10.1109/ISBI.2019.8759422

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M3 - Conference contribution

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T3 - Proceedings - International Symposium on Biomedical Imaging

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BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019

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ER -

Bladder cancer multi-class segmentation in MRI with pyramid-in-pyramid network

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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