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Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets

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Abstract

Ischemic stroke segmentation at an acute stage is vital in assessing the severity of patients’ impairment and guiding therapeutic decision-making for reperfusion. Although many deep learning studies have shown attractive performance in medical segmentation, it is difficult to use these models trained on public data with private hospitals’ datasets. Here, we demonstrate an ensemble model that employs two different multimodal approaches for generalization, a more effective way to perform on external datasets. First, after we jointly train a segmentation model on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) MR modalities, the model is inferred on the DWI images. Second, a channel-wise segmentation model is trained by concatenating the DWI and ADC images as input, and then is inferred using both MR modalities. Before training with ischemic stroke data, we utilized BraTS 2021, a public brain tumor dataset, for transfer learning. An extensive ablation study evaluates which strategy learns better representations for ischemic stroke segmentation. In our study, nnU-Net well-known for robustness is selected as our baseline model. Our proposed method is evaluated on three different datasets: the Asan Medical Center (AMC) I and II, and the 2022 Ischemic Stroke Lesion Segmentation (ISLES). Our experiments are widely validated over a large, multi-center, and multi-scanner dataset with a huge amount of 846 scans. Not only stroke lesion models can benefit from transfer learning using brain tumor data, but combining the MR modalities using different training schemes also highly improves segmentation performance. The method achieved a top-1 ranking in the ongoing ISLES’22 challenge and performed particularly well on lesion-wise metrics of interest to neuroradiologists, achieving a Dice coefficient of 78.69% and a lesion-wise F1 score of 82.46%. Also, the method was relatively robust on the AMC I (Dice, 60.35%; lesion-wise F1, 68.30%) and II (Dice; 74.12%; lesion-wise F1, 67.53%) datasets in different settings. The high segmentation accuracy of our proposed method could improve radiologists’ ability to detect ischemic stroke lesions in MRI images. Our model weights and inference code are available on https://github.com/MDOpx/ISLES22-model-inference.

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Funding

This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (2023R1A2C3004880) and the Ministry of Education (2020R1A6A1A03047902); by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (1711195277, RS-2020-KD000008); by National R&D program through the NRF funded by the Ministry of Science and ICT (2021M3C1C3097624); by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019–0-01906, Artificial Intelligence Graduate School Program (POSTECH)) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE); by the BK21 FOUR project.

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Authors and Affiliations

  1. Graduate School of Artificial Intelligence (GSAI), Department of Electrical Engineering, Medical Science and Engineering, and Medical Device Innovation Center, Convergence IT Engineering, Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea

    Hyunsu Jeong, Hyunseok Lim, Chiho Yoon & Chulhong Kim

  2. Department of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Jongjun Won & Grace Yoojin Lee

  3. icometrix, Leuven, Belgium

    Ezequiel de la Rosa

  4. Department of Informatics, Technical University of Munich, Neuroradiology Munich, Germany

    Ezequiel de la Rosa & Jan S. Kirschke

  5. Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechtsder Isar, Technical University of Munich, Munich, Germany

    Jan S. Kirschke

  6. Department of Biomedical Engineering, Convergence Medicine, Radiology, Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Bumjoon Kim & Namkug Kim

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  1. Hyunsu Jeong
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Contributions

Hyunsu Jeong: conceptualization, methodology, software, investigation, validation, formal analysis, writing—original draft, writing—review and editing. Hyunseok Lim: software, visualization, writing—review and editing. Chiho Yoon: software, writing—review and editing. Jongjun Won: supervision, writing—review and editing. Grace Yoojin Lee: supervision, writing—review and editing. Ezequiel de la Rosa: formal analysis. Jan S. Kirschke: formal analysis. Bumjoon Kim: data curation, supervision, writing—review and editing. Namkug Kim: data curation, supervision, writing—review and editing. Chulhong Kim: supervision, writing—review and editing, funding acquisition.

Corresponding authors

Correspondence to Bumjoon Kim, Namkug Kim or Chulhong Kim.

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Ethics Approval

The AMC I and II datasets were collected according to the principles of the Declaration of Helsinki, and the data collection was performed in accordance with current scientific guidelines. The study protocol was approved by the Institutional Review Board (IRB) Committee of AMC, University of Ulsan College of Medicine, Seoul, Republic of Korea. The requirement for informed patient consent was waived by the IRB Committee of AMC.

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Informed consent was obtained from all individual participants included in the study.

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The authors affirm that human research participants provided informed consent for publication of the images in Figs. 1, 2, 3 and 4.

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Jeong, H., Lim, H., Yoon, C. et al. Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01099-6

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