Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation

Zhiqi Zhao; Danni Ai; Wenjie Li; Jingfan Fan; Hong Song; Yongtian Wang; Jian Yang

doi:10.1007/978-3-030-34110-7_42

Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation

Zhiqi Zhao, Danni Ai^*, Wenjie Li, Jingfan Fan, Hong Song, Yongtian Wang, Jian Yang

^*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Image-guided surgery has become an important aid in sinus and skull base surgery. In the preoperative planning stage, vital structures, such as the visual pathway, must be segmented to guide the surgeon during surgery. However, owing to the elongated structure and low contrast in medical images, automatic segmentation of the visual pathway is challenging. This study proposed a novel method based on 3D fully convolutional network (FCN) combined with a spatial probabilistic distribution map (SPDM) for visual pathway segmentation in magnetic resonance imaging. Experimental results indicated that compared with the FCN that relied only on image intensity information, the introduction of an SPDM effectively overcame the problem of low contrast and blurry boundary and achieved better segmentation performance.

Original language	English
Title of host publication	Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2
Editors	Yao Zhao, Chunyu Lin, Nick Barnes, Baoquan Chen, Rüdiger Westermann, Xiangwei Kong
Publisher	Springer
Pages	509-518
Number of pages	10
ISBN (Print)	9783030341091
DOIs	https://doi.org/10.1007/978-3-030-34110-7_42
Publication status	Published - 2019
Event	10th International Conference on Image and Graphics, ICIG 2019 - Beijing, China Duration: 23 Aug 2019 → 25 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11902 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Image and Graphics, ICIG 2019
Country/Territory	China
City	Beijing
Period	23/08/19 → 25/08/19

Keywords

3D fully convolutional network
Optic nerve
Spatial probabilistic distribution map
Visual pathway segmentation

Access to Document

10.1007/978-3-030-34110-7_42

Cite this

Zhao, Z., Ai, D., Li, W., Fan, J., Song, H., Wang, Y., & Yang, J. (2019). Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation. In Y. Zhao, C. Lin, N. Barnes, B. Chen, R. Westermann, & X. Kong (Eds.), Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2 (pp. 509-518). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11902 LNCS). Springer. https://doi.org/10.1007/978-3-030-34110-7_42

Zhao, Zhiqi ; Ai, Danni ; Li, Wenjie et al. / Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation. Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2. editor / Yao Zhao ; Chunyu Lin ; Nick Barnes ; Baoquan Chen ; Rüdiger Westermann ; Xiangwei Kong. Springer, 2019. pp. 509-518 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{bd5fc6747d78474fabee789195dd9bc7,

title = "Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation",

abstract = "Image-guided surgery has become an important aid in sinus and skull base surgery. In the preoperative planning stage, vital structures, such as the visual pathway, must be segmented to guide the surgeon during surgery. However, owing to the elongated structure and low contrast in medical images, automatic segmentation of the visual pathway is challenging. This study proposed a novel method based on 3D fully convolutional network (FCN) combined with a spatial probabilistic distribution map (SPDM) for visual pathway segmentation in magnetic resonance imaging. Experimental results indicated that compared with the FCN that relied only on image intensity information, the introduction of an SPDM effectively overcame the problem of low contrast and blurry boundary and achieved better segmentation performance.",

keywords = "3D fully convolutional network, Optic nerve, Spatial probabilistic distribution map, Visual pathway segmentation",

author = "Zhiqi Zhao and Danni Ai and Wenjie Li and Jingfan Fan and Hong Song and Yongtian Wang and Jian Yang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 10th International Conference on Image and Graphics, ICIG 2019 ; Conference date: 23-08-2019 Through 25-08-2019",

year = "2019",

doi = "10.1007/978-3-030-34110-7_42",

language = "English",

isbn = "9783030341091",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "509--518",

editor = "Yao Zhao and Chunyu Lin and Nick Barnes and Baoquan Chen and R{\"u}diger Westermann and Xiangwei Kong",

booktitle = "Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2",

address = "Germany",

}

Zhao, Z, Ai, D, Li, W, Fan, J , Song, H , Wang, Y & Yang, J 2019, Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation. in Y Zhao, C Lin, N Barnes, B Chen, R Westermann & X Kong (eds), Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11902 LNCS, Springer, pp. 509-518, 10th International Conference on Image and Graphics, ICIG 2019, Beijing, China, 23/08/19. https://doi.org/10.1007/978-3-030-34110-7_42

Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation. / Zhao, Zhiqi; Ai, Danni; Li, Wenjie et al.
Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2. ed. / Yao Zhao; Chunyu Lin; Nick Barnes; Baoquan Chen; Rüdiger Westermann; Xiangwei Kong. Springer, 2019. p. 509-518 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11902 LNCS).

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

TY - GEN

T1 - Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation

AU - Zhao, Zhiqi

AU - Ai, Danni

AU - Li, Wenjie

AU - Fan, Jingfan

AU - Song, Hong

AU - Wang, Yongtian

AU - Yang, Jian

PY - 2019

Y1 - 2019

N2 - Image-guided surgery has become an important aid in sinus and skull base surgery. In the preoperative planning stage, vital structures, such as the visual pathway, must be segmented to guide the surgeon during surgery. However, owing to the elongated structure and low contrast in medical images, automatic segmentation of the visual pathway is challenging. This study proposed a novel method based on 3D fully convolutional network (FCN) combined with a spatial probabilistic distribution map (SPDM) for visual pathway segmentation in magnetic resonance imaging. Experimental results indicated that compared with the FCN that relied only on image intensity information, the introduction of an SPDM effectively overcame the problem of low contrast and blurry boundary and achieved better segmentation performance.

AB - Image-guided surgery has become an important aid in sinus and skull base surgery. In the preoperative planning stage, vital structures, such as the visual pathway, must be segmented to guide the surgeon during surgery. However, owing to the elongated structure and low contrast in medical images, automatic segmentation of the visual pathway is challenging. This study proposed a novel method based on 3D fully convolutional network (FCN) combined with a spatial probabilistic distribution map (SPDM) for visual pathway segmentation in magnetic resonance imaging. Experimental results indicated that compared with the FCN that relied only on image intensity information, the introduction of an SPDM effectively overcame the problem of low contrast and blurry boundary and achieved better segmentation performance.

KW - 3D fully convolutional network

KW - Optic nerve

KW - Spatial probabilistic distribution map

KW - Visual pathway segmentation

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U2 - 10.1007/978-3-030-34110-7_42

DO - 10.1007/978-3-030-34110-7_42

M3 - Conference contribution

AN - SCOPUS:85076922123

SN - 9783030341091

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 509

EP - 518

BT - Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2

A2 - Zhao, Yao

A2 - Lin, Chunyu

A2 - Barnes, Nick

A2 - Chen, Baoquan

A2 - Westermann, Rüdiger

A2 - Kong, Xiangwei

PB - Springer

T2 - 10th International Conference on Image and Graphics, ICIG 2019

Y2 - 23 August 2019 through 25 August 2019

ER -

Zhao Z, Ai D, Li W, Fan J , Song H , Wang Y et al. Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation. In Zhao Y, Lin C, Barnes N, Chen B, Westermann R, Kong X, editors, Image and Graphics - 10th International Conference, ICIG 2019, Proceedings, Part 2. Springer. 2019. p. 509-518. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34110-7_42

Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation

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