A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)

Jun Ho Kim; Mohammed A. Al-Masni; Hae Joon Lee; Yoon Seok Choi; Dong Hyun Kim

doi:10.1109/AICAS54282.2022.9869855

A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)

Jun Ho Kim
, Mohammed A. Al-Masni
, Hae Joon Lee
, Yoon Seok Choi
, Dong Hyun Kim

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

5 Scopus citations

Abstract

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases including cognitive decline, intracerebral hemorrhage, cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process. In this paper, we propose an efficient single-stage deep learning framework for automatic detection of the CMBs. The framework consists of a 3D U-Net and a region proposal network employing a feature fusing method (FFRP-Net) for detecting small objects. This model utilizes Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D contextual information. The performance of the proposed FFRP-Net records a sensitivity of 94.66% and an average number of false positives per subject (FPavg) of 8.82.

Original language	English
Title of host publication	Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	258-261
Number of pages	4
ISBN (Electronic)	9781665409964
DOIs	https://doi.org/10.1109/AICAS54282.2022.9869855
State	Published - 2022
Externally published	Yes
Event	4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of Duration: 13 Jun 2022 → 15 Jun 2022

Publication series

Name	Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022

Conference

Conference	4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Country/Territory	Korea, Republic of
City	Incheon
Period	13/06/22 → 15/06/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cerebral Microbleeds
Deep Learning
Feature Fusion
Region Proposal Network

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture
Human-Computer Interaction
Electrical and Electronic Engineering

Access to Document

10.1109/AICAS54282.2022.9869855

Cite this

Kim, J. H., Al-Masni, M. A., Lee, H. J., Choi, Y. S., & Kim, D. H. (2022). A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net). In Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 (pp. 258-261). (Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS54282.2022.9869855

Kim, Jun Ho ; Al-Masni, Mohammed A. ; Lee, Hae Joon et al. / A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net). Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 258-261 (Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022).

@inproceedings{556d654aef0140968a5da9c04167222f,

title = "A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)",

abstract = "Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases including cognitive decline, intracerebral hemorrhage, cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process. In this paper, we propose an efficient single-stage deep learning framework for automatic detection of the CMBs. The framework consists of a 3D U-Net and a region proposal network employing a feature fusing method (FFRP-Net) for detecting small objects. This model utilizes Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D contextual information. The performance of the proposed FFRP-Net records a sensitivity of 94.66\% and an average number of false positives per subject (FPavg) of 8.82.",

keywords = "Cerebral Microbleeds, Deep Learning, Feature Fusion, Region Proposal Network",

author = "Kim, \{Jun Ho\} and Al-Masni, \{Mohammed A.\} and Lee, \{Hae Joon\} and Choi, \{Yoon Seok\} and Kim, \{Dong Hyun\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 ; Conference date: 13-06-2022 Through 15-06-2022",

year = "2022",

doi = "10.1109/AICAS54282.2022.9869855",

language = "English",

series = "Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022",

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Kim, JH, Al-Masni, MA, Lee, HJ, Choi, YS & Kim, DH 2022, A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net). in Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022. Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 258-261, 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, Incheon, Korea, Republic of, 13/06/22. https://doi.org/10.1109/AICAS54282.2022.9869855

A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net). / Kim, Jun Ho; Al-Masni, Mohammed A.; Lee, Hae Joon et al.
Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 258-261 (Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022).

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

TY - GEN

T1 - A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)

AU - Kim, Jun Ho

AU - Al-Masni, Mohammed A.

AU - Lee, Hae Joon

AU - Choi, Yoon Seok

AU - Kim, Dong Hyun

PY - 2022

Y1 - 2022

N2 - Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases including cognitive decline, intracerebral hemorrhage, cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process. In this paper, we propose an efficient single-stage deep learning framework for automatic detection of the CMBs. The framework consists of a 3D U-Net and a region proposal network employing a feature fusing method (FFRP-Net) for detecting small objects. This model utilizes Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D contextual information. The performance of the proposed FFRP-Net records a sensitivity of 94.66% and an average number of false positives per subject (FPavg) of 8.82.

AB - Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to cerebrovascular diseases including cognitive decline, intracerebral hemorrhage, cerebral infarction. However, manual detection of the CMBs is a time-consuming and error-prone process. In this paper, we propose an efficient single-stage deep learning framework for automatic detection of the CMBs. The framework consists of a 3D U-Net and a region proposal network employing a feature fusing method (FFRP-Net) for detecting small objects. This model utilizes Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D contextual information. The performance of the proposed FFRP-Net records a sensitivity of 94.66% and an average number of false positives per subject (FPavg) of 8.82.

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KW - Feature Fusion

KW - Region Proposal Network

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M3 - Conference contribution

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BT - Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 June 2022 through 15 June 2022

ER -

Kim JH, Al-Masni MA, Lee HJ, Choi YS, Kim DH. A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net). In Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 258-261. (Proceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022). doi: 10.1109/AICAS54282.2022.9869855

A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this