Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

被引：0
作者：
Raju C.S.P. [1 ]
Neelapu B.C. [2 ]
Laskar R.H. [1 ]
Muhammad G. [3 ]
机构：
[1] Speech and Image Processing Lab, Department of ECE, National Institute of Technology Silchar, Assam, Silchar
[2] Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, Rourkela
[3] Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh
来源：
Multimedia Tools and Applications | 2025年 / 84卷 / 10期
关键词：
Comparative analysis; CT perfusion; Deep learning; Ischemic stroke segmentation; Multimodal fusion strategies;
D O I：
10.1007/s11042-024-19252-2
中图分类号：
学科分类号：
摘要：
Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
引用
收藏
页码：7493 / 7518
页数：25
相关论文
共 45 条
  • [1] Abulnaga S.M., Rubin J., Ischemic stroke lesion segmentation in ct perfusion scans using pyramid pooling and focal loss, Crimi A. Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries Springer International Publishing, pp. 352-363, (2019)
  • [2] Al Jowair H., Alsulaiman M., Muhammad G., Multi parallel u-net encoder network for effective polyp image segmentation, Image Vis Comput, 137, (2023)
  • [3] Alshehri F., Muhammad G., A few-shot learning-based ischemic stroke segmentation system using weighted mri fusion, Image Vis Comput, 140, (2023)
  • [4] Anand V.K., Khened M., Alex V., Krishnamurthi G., Fully Automatic Segmentation for Ischemic Stroke Using Ct Perfusion, pp. 328-334, (2019)
  • [5] Bertels J., Robben D., Vandermeulen D., Suetens P., Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke, 11383, (2019)
  • [6] Chen L., Bentley P., Rueckert D., Fully automatic acute ischemic lesion segmentation in dwi using convolutional neural networks, Neuroimage Clin, 15, pp. 633-643, (2017)
  • [7] Chen Y., Chen J., Wei D., Li Y., Zheng Y., Octopusnet: A deep learning segmentation network for multi-modal medical images, Multiscale Multimodal Medical Imaging Springer International Publishing, pp. 17-25, (2020)
  • [8] Clerigues A., Valverde S., Bernal J., Freixenet J., Oliver A., Llado X., Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks, Comput Biol Med, 115, (2019)
  • [9] Dolz J., Ben Ayed I., Desrosiers C., Dense multi-path u-net for ischemic stroke lesion segmentation in multiple image modalities, Crimi A. Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries, pp. 271-282, (2019)
  • [10] Ghnemat R., Khalil A., Abu Al-Haija Q., Ischemic Stroke Lesion Segmentation Using Mutation Model and Generative Adversarial Network, Electronics (Switzerland), 12, (2023)
12345