Editorial for "A Lightweight Convolutional Neural Network Based on Dynamic Level-Set Loss Function for Spine MR Image Segmentation"

Affiliations

¹ Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan.
² Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Editorial

Hayaru Shouno et al. J Magn Reson Imaging. 2024 Apr.

. 2024 Apr;59(4):1454-1455.

doi: 10.1002/jmri.28878. Epub 2023 Jun 27.

¹ Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu, Tokyo, Japan.
² Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

No abstract available

Comment on

A Lightweight Convolutional Neural Network Based on Dynamic Level-Set Loss Function for Spine MR Image Segmentation.
He S, Li Q, Li X, Zhang M. He S, et al. J Magn Reson Imaging. 2024 Apr;59(4):1438-1453. doi: 10.1002/jmri.28877. Epub 2023 Jun 29. J Magn Reson Imaging. 2024. PMID: 37382232

1. He S, Li Q, Li X, Zhang M. Dynamic level-set loss function based lightweight convolutional neural network for spine MR image segmentation. J Magn Reson Imaging 2024;59(4):1438-1453.
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1. He S, Li Q, Li X, Zhang M. LSW-Net: Lightweight deep neural network based on small-world properties for spine MR image segmentation. J Magn Reson Imaging 2024;58(6):1762-1776.
1. He S, Li Q, Li X, Zhang M. An optimized segmentation convolutional neural network with dynamic energy loss function for 3D reconstruction of lumbar spine MR images. Comput Biol Med 2023;160:106839.
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