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Preliminary study of substantia nigra analysis by tensorial feature extraction

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients.

Method

We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis.

Results

The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD.

Conclusions

We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

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Notes

  1. This decomposition is also called as CANDECOMP, PARAFAC or CANDECOMP/PARAFAC (CP).

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Acknowledgements

This study was funded by grants from AMED (22dm0307101h0004), MEXT/JSPS KAKENHI (21K19898, 23K16900).

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Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Science, Fukuoka University, Nanakuma 8-19-1, Jonan-ku, Fukuoka, 814-0180, Japan

    Hayato Itoh

  2. Department of Neurology, Institute of Medicine, University of Tsukuba, Tennodai 1-1-1, Tsukuba, 305-8575, Japan

    Shinji Saiki

  3. Department of Neurology, Juntendo University School of Medicine, Hongo 2-1-1, Bunkyo-ku, Tokyo, 113-8421, Japan

    Wataru Sako, Kei-ichi Ishikawa & Nobutaka Hattori

  4. Department of Radiology, Juntendo University Graduate School of Medicine, Hongo 2-1-1, Bunkyo-ku, Tokyo, 113-8421, Japan

    Koji Kamagata & Shigeki Aoki

  5. Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Hayato Itoh, Masahiro Oda & Kensaku Mori

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  1. Hayato Itoh
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  2. Masahiro Oda
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Correspondence to Hayato Itoh.

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Conflict of interest

Mori K is supported by Cybernet Systems and Olympus (research grant) and by NTT outside the submitted work. The other authors have no conflict of interest.

Ethical approval

All the procedures performed in studies involving human participants were in accordance with the ethical committees of Nagoya University (No. 382) and Juntendo University (H19-179) and the 1964 Helsinki Declaration and subsequent amendments or comparable ethical standards. Informed consent was obtained by an opt-out procedure from all individual participants in this study.

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Itoh, H., Oda, M., Saiki, S. et al. Preliminary study of substantia nigra analysis by tensorial feature extraction. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03175-2

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