Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets.
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 traine...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2375 - 2390 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515405&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515405 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515405 181515405 181515405 10.1007/s10278-024-01099-6 181515405 ppf: 2375 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets. aug: au: Jeong, Hyunsu Lim, Hyunseok Yoon, Chiho Won, Jongjun Lee, Grace Yoojin de la Rosa, Ezequiel Kirschke, Jan S. Kim, Bumjoon Kim, Namkug Kim, Chulhong affil: https://ror.org/04xysgw12 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 sug: subj: Ischemic Stroke Brain Neoplasms Magnetic Resonance Imaging Image Processing, Computer Assisted Methods Ensemble Learning Neuroradiography Human Mann-Whitney U Test Wilcoxon Signed Rank Test Descriptive Statistics Deep Learning Funding Source Imaging, Three-Dimensional Image Interpretation, Computer Assisted Minimum Data Set Experimental Studies Radiologists Multicenter Studies ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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