Next Article in Journal
Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features
Previous Article in Journal
High-Sensitivity Troponin T: A Potential Safety Predictive Biomarker for Discharge from the Emergency Department of Patients with Confirmed Influenza
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 12
Issue 4
10.3390/jpm12040521
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation

by
Seung-Ah Lee
1,†,
Jae-Won Jang
2,3,4,†,
Sang-Won Park
2,3,4,
Pum-Jun Kim
5,
Na-Young Yeo
6,
Chulho Kim
7,
Yoon Kim
8,9,
Hyun-Soo Choi
2,8,9,* and
Seongheon Kim
3,4,*
1
Department of Computer and Communications Engineering, Kangwon National University, Chuncheon 24253, Korea
2
Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon 24253, Korea
3
School of Medicine, Kangwon National University, Chuncheon 24253, Korea
4
Department of Neurology, Kangwon National University Hospital, Chuncheon 24253, Korea
5
Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon 24252, Korea
6
Chuncheon Artificial Intelligence Center, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea
7
Department of Neurology, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea
8
Department of Computer Science and Engineering, Kangwon National University, Chuncheon 24253, Korea
9
ZIOVISION, Chuncheon 24341, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Pers. Med. 2022, 12(4), 521; https://doi.org/10.3390/jpm12040521
Submission received: 20 February 2022 / Revised: 16 March 2022 / Accepted: 21 March 2022 / Published: 24 March 2022
(This article belongs to the Topic Complex Systems and Artificial Intelligence)
Browse Figures
Versions Notes

Abstract

The accurate estimation of acute ischemic stroke (AIS) using diffusion-weighted imaging (DWI) is crucial for assessing patients and guiding treatment options. This study aimed to propose a method that estimates AIS volume in DWI objectively, quickly, and accurately. We used a dataset of DWI with AIS, including 2159 participants (1179 for internal validation and 980 for external validation) with various types of AIS. We constructed algorithms using 3D segmentation (direct estimation) and 2D segmentation (indirect estimation) and compared their performances with those annotated by neurologists. The proposed pretrained indirect model demonstrated higher segmentation performance than the direct model, with a sensitivity, specificity, F1-score, and Jaccard index of 75.0%, 77.9%, 76.0, and 62.1%, respectively, for internal validation, and 72.8%, 84.3%, 77.2, and 63.8%, respectively, for external validation. Volume estimation was more reliable for the indirect model, with 93.3% volume similarity (VS), 0.797 mean absolute error (MAE) for internal validation, VS of 89.2% and a MAE of 2.5% for external validation. These results suggest that the indirect model using 2D segmentation developed in this study can provide an accurate estimation of volume from DWI of AIS and may serve as a supporting tool to help physicians make crucial clinical decisions.
Keywords: acute ischemic stroke; computer-aided diagnosis; segmentation; deep-learning acute ischemic stroke; computer-aided diagnosis; segmentation; deep-learning
Graphical Abstract

Share and Cite

MDPI and ACS Style

Lee, S.-A.; Jang, J.-W.; Park, S.-W.; Kim, P.-J.; Yeo, N.-Y.; Kim, C.; Kim, Y.; Choi, H.-S.; Kim, S. Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation. J. Pers. Med. 2022, 12, 521. https://doi.org/10.3390/jpm12040521

AMA Style

Lee S-A, Jang J-W, Park S-W, Kim P-J, Yeo N-Y, Kim C, Kim Y, Choi H-S, Kim S. Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation. Journal of Personalized Medicine. 2022; 12(4):521. https://doi.org/10.3390/jpm12040521

Chicago/Turabian Style

Lee, Seung-Ah, Jae-Won Jang, Sang-Won Park, Pum-Jun Kim, Na-Young Yeo, Chulho Kim, Yoon Kim, Hyun-Soo Choi, and Seongheon Kim. 2022. "Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation" Journal of Personalized Medicine 12, no. 4: 521. https://doi.org/10.3390/jpm12040521

APA Style

Lee, S.-A., Jang, J.-W., Park, S.-W., Kim, P.-J., Yeo, N.-Y., Kim, C., Kim, Y., Choi, H.-S., & Kim, S. (2022). Indirect Volume Estimation for Acute Ischemic Stroke from Diffusion Weighted Image Using Slice Image Segmentation. Journal of Personalized Medicine, 12(4), 521. https://doi.org/10.3390/jpm12040521

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop