TY - GEN
T1 - SE-ResNet based vulnerable plaque recognition in IVOCT images
AU - Qi, Wenliu
AU - Du, Sihui
AU - Tang, Xiaoying
AU - Wang, Ancong
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/11/10
Y1 - 2022/11/10
N2 - Acute coronary syndrome (ACS) caused by vulnerable plaques can lead to sudden death. The resolution of intravascular optical coherence tomography (IVOCT) is up to 10 μm, and it has become the first choice for vulnerable plaque recognition. However, it is time-consuming and burdensome for doctors to label vulnerable plaques manually. As a result, it is important to develop an automatic method for vulnerable plaque recognition in IVOCT images. This paper proposes a lightweight and real-time method to identify the main vulnerable plaque areas in IVOCT images. The accuracy rate, recall rate and overlap rate of this method on the test set are 84.8%, 90.1%, and 87.0% respectively, and the recognition quality is 87.2%. The results suggest that our method may assist doctors to recognize vulnerable plaque areas fast and accurately.
AB - Acute coronary syndrome (ACS) caused by vulnerable plaques can lead to sudden death. The resolution of intravascular optical coherence tomography (IVOCT) is up to 10 μm, and it has become the first choice for vulnerable plaque recognition. However, it is time-consuming and burdensome for doctors to label vulnerable plaques manually. As a result, it is important to develop an automatic method for vulnerable plaque recognition in IVOCT images. This paper proposes a lightweight and real-time method to identify the main vulnerable plaque areas in IVOCT images. The accuracy rate, recall rate and overlap rate of this method on the test set are 84.8%, 90.1%, and 87.0% respectively, and the recognition quality is 87.2%. The results suggest that our method may assist doctors to recognize vulnerable plaque areas fast and accurately.
KW - Convolutional neural network
KW - Intravascular optical coherence tomography
KW - Vulnerable plaque
KW - image recognition
UR - http://www.scopus.com/inward/record.url?scp=85150394865&partnerID=8YFLogxK
U2 - 10.1145/3574198.3574209
DO - 10.1145/3574198.3574209
M3 - Conference contribution
AN - SCOPUS:85150394865
T3 - ACM International Conference Proceeding Series
SP - 68
EP - 73
BT - ICBBE 2022 - Proceeding of 2022 9th International Conference on Biomedical and Bioinformatics Engineering
PB - Association for Computing Machinery
T2 - 9th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2022
Y2 - 10 November 2022 through 13 November 2022
ER -