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Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs

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Abstract

Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural networks in particular, have rapidly become preferred options in medical image analysis because of their state-of-the-art performance. However, the performances of conventional and deep learning-based methods cannot be compared reliably because of their evaluations on different datasets. Hence, we developed both conventional and deep learning-based methods for lung vessel segmentation and chest radiograph registration, and subsequently compared their performances on the same datasets. The results strongly indicated the superiority of deep learning-based methods over their conventional counterparts.

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Authors and Affiliations

  1. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Wei Guo & Qiang Li

  2. School of Computer, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China

    Wei Guo

  3. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaomeng Gu & Qiming Fang

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  1. Wei Guo
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  2. Xiaomeng Gu
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  3. Qiming Fang
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  4. Qiang Li
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Correspondence to Qiang Li.

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Guo, W., Gu, X., Fang, Q. et al. Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs. Radiol Phys Technol 14, 6–15 (2021). https://doi.org/10.1007/s12194-020-00584-1

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  • DOI: https://doi.org/10.1007/s12194-020-00584-1

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