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

27 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

UN SDGs

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

SDG 3 Good Health and Well-being

Free Keywords

Bladder cancer
Deep learning
MRI
Segmentation

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

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

Y1 - 2019/4

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

UR - https://www.scopus.com/pages/publications/85073899433

U2 - 10.1109/ISBI.2019.8759422

DO - 10.1109/ISBI.2019.8759422

M3 - Conference contribution

AN - SCOPUS:85073899433

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 28

EP - 31

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

PB - IEEE Computer Society

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

Y2 - 8 April 2019 through 11 April 2019

ER -

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

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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