光干渉断層計画像から緑内障の視野を推測する３次元畳み込みニューラルネットワークモデルの構築
DOI:
https://doi.org/10.51094/jxiv.170キーワード:
光干渉断層計、 緑内障、 視野、 深層学習抄録
目的: 光干渉断層計（OCT）画像から視野を推定し、その精度を向上させるため、セグメンテーション不要の3次元畳み込みニューラルネットワークモデル（3DCNN）により学習を行った。
方法: 単一施設の後ろ向き全例調査である。OCTに加えハンフリー視野計(HFA) 24-2もしくは10-2検査を受けた、すべてのタイプの緑内障または緑内障の疑いの患者を含む連続3,416人（6,356眼）を調査した。学習すべき視野及び視野の傾きの教師値は、視野閾値の測定点毎の回帰直線から算出した。学習対象と評価対象を分け、学習時に低signal strength index(SSI)及び眼内疾患合併の有無で４群に分けて3DCNNモデルによる５分割交差検証を施行し、同一条件の評価対象で最良の結果が得られた学習条件で10分割交差検証を施行した。
結果:５分割交差検証の結果、全てのSSI及び眼内疾患を含んで学習した群がいずれの対象に対しても最も推測性能が高く、同条件での10分割交差検証の結果、緑内障患者のペアデータあたりの二乗平均平方根誤差は、HFA24-2で3.27±2.20dB［平均 ± 標準偏差］、HFA10-2で3.02±2.28dBであった。
結論: 今回のモデルにより、OCT画像からの高い視野推測精度を達成した。容易に症例数を増やす事ができるため、今後の多施設共同研究が期待される。
臨床への橋渡し: 本モデルは、OCT画像から高い精度で視野を推定できるため、患者の負担を軽減することが可能である。
ダウンロード *前日までの集計結果を表示します
引用文献
Weinreb, R. N. et al. Primary open-angle glaucoma. Nat Rev Dis Primers 2, 1–19 (2016).
Allison, K., Patel, D. & Alabi, O. Epidemiology of Glaucoma: The Past, Present, and Predictions for the Future. Cureus 12, (2020).
Altangerel, U., Spaeth, G. L. & Rhee, D. J. Visual function, disability, and psychological impact of glaucoma. Curr Opin Ophthalmol vol. 14 (2003).
Wang, M. et al. Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence. JAMA Ophthalmol 138, (2020).
Blumberg, D. M. et al. Association between undetected 10-2 visual field damage and vision-related quality of life in patients with glaucoma. JAMA Ophthalmol 135, (2017).
Bansal, M., Arora, T. & Dada, T. Effect of a novel computer software simulating humphrey visual field (HVF) on patient performance of HVF. Invest Ophthalmol Vis Sci 55, (2014).
Hashimoto, Y. et al. Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field. Transl Vis Sci Technol 10, (2021).
Asaoka, R. et al. A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT. Ophthalmology Science 1, 100055 (2021).
Cirafici, P. et al. Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma. Eye (Basingstoke) 35, 868–876 (2021).
Shin, J., Kim, S., Kim, J. & Park, K. Visual field inference from optical coherence tomography using deep learning algorithms: A comparison between devices. Transl Vis Sci Technol 10, (2021).
Sugiura, H. et al. Estimating glaucomatous visual sensitivity from retinal thickness with pattern-based regularization and visualization. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 783–792 (Association for Computing Machinery, 2018). doi:10.1145/3219819.3219866.
Xu, L. et al. Improving Visual Field Trend Analysis with OCT and Deeply Regularized Latent-Space Linear Regression. Ophthalmol Glaucoma 4, (2021).
Xu, L. et al. PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 3826–3834 (Association for Computing Machinery, 2021). doi:10.1145/3447548.3467195.
Xu, L. et al. Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression. Am J Ophthalmol 218, (2020).
Hashimoto, Y. et al. Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma. British Journal of Ophthalmology 105, (2021).
Asano, S. et al. Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images. Sci Rep 11, (2021).
Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G. & Murphy, K. Deep learning for chest X-ray analysis: A survey. Med Image Anal 72, (2021).
Harwerth, R. S., Wheat, J. L., Fredette, M. J. & Anderson, D. R. Linking structure and function in glaucoma. Prog Retin Eye Res 29, 249–271 (2010).
Garcia-Medina, J. J., Rotolo, M., Rubio-Velazquez, E., Pinazo-Duran, M. D. & Del-Rio-vellosillo, M. Macular structure–function relationships of all retinal layers in primary open-angle glaucoma assessed by microperimetry and 8 × 8 posterior pole analysis of oct. J Clin Med 10, (2021).
Akashi, A. et al. The ability of SD-OCT to differentiate early glaucoma with high myopia from highly myopic controls and nonhighly myopic controls. Invest Ophthalmol Vis Sci 56, (2015).
Sezgin Akcay, B. I., Gunay, B. O., Kardes, E., Unlu, C. & Ergin, A. Evaluation of the Ganglion Cell Complex and Retinal Nerve Fiber Layer in Low, Moderate, and High Myopia: A Study by RTVue Spectral Domain Optical Coherence Tomography. Semin Ophthalmol 32, (2017).
Yu, H. H. et al. Estimating Global Visual Field Indices in Glaucoma by Combining Macula and Optic Disc OCT Scans Using 3-Dimensional Convolutional Neural Networks. Ophthalmol Glaucoma 4, 102–112 (2021).
Anderson, A. J. Significant glaucomatous visual field progression in the first two years: What does it mean? Transl Vis Sci Technol 5, (2016).
Murata, H., Araie, M. & Asaoka, R. A new approach to measure visual field progression in glaucoma patients using variational bayes linear regression. Invest Ophthalmol Vis Sci 55, (2014).
Yu, S. et al. Comparison of SITA Faster 24-2C test times to legacy SITA tests. Invest Ophthalmol Vis Sci 60, (2019).
L.J., S., R.A., R. & D.P., C. Standard or Fast?-Differences in precision between SITA Standard and SITA Fast testing algorithms and their utility for detecting visual field deterioration. Invest Ophthalmol Vis Sci 55, (2014).
Saunders, L. J., Russell, R. A. & Crabb, D. P. Measurement precision in a series of visual fields acquired by the standard and fast versions of the swedish interactive thresholding algorithm analysis of large-scale data from clinics. JAMA Ophthalmol 133, (2015).
Anderson DR, P. V. Automated Static Perimetry, 2nd edtion. (Mosby, St. Louis, 1999:121-190).
Nakatani, Y., Higashide, T., Ohkubo, S., Takeda, H. & Sugiyama, K. Effect of cataract and its removal on ganglion cell complex thickness and peripapillary retinal nerve fiber layer thickness measurements by fourier-domain optical coherence tomography. J Glaucoma 22, 447–455 (2013).
Kim, M., Eom, Y., Song, J. S. & Kim, H. M. Effect of Cataract Grade according to Wide-Field Fundus Images on Measurement of Macular Thickness in Cataract Patients. Korean Journal of Ophthalmology 32, (2018).
shijianjian. EfficientNet-PyTorch-3D. github.com. Updated July 20, 2021. Accessed Aug 10, 2022. https://github.com/shijianjian/EfficientNet-PyTorch-3D (2021).
Tan, M. & Le, Q. v. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv preprint:1905.11946 (2019).
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv:1412.6980 (2014).
Konar, J., Khandelwal, P. & Tripathi, R. Comparison of Various Learning Rate Scheduling Techniques on Convolutional Neural Network. in 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2020 (Institute of Electrical and Electronics Engineers Inc., 2020). doi:10.1109/SCEECS48394.2020.94.
Liu, L. et al. On the Variance of the Adaptive Learning Rate and Beyond. arXiv:1908.03265 (2019).
Qian, X. & Klabjan, D. The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent. arXiv:2004.13146 (2020).
Wu, H. & Gu, X. Towards dropout training for convolutional neural networks. Neural Networks 71, (2015).
Loshchilov, I. & Hutter, F. Decoupled Weight Decay Regularization. arXiv:1711.05101 (2017).
Zhuang, J. et al. AdaBelief optimizer: adapting stepsizes by the belief in observed gradients. NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems 18795–18806 (2020).
Qian, N. On the momentum term in gradient descent learning algorithms. Neural Networks 12, 145–151 (1999).
Foret, P., Kleiner, A., Mobahi, H. & Neyshabur, B. Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv:2010.01412 (2020).
Kwon, J., Kim, J., Park, H. & Choi, I. K. ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks. arXiv:2102.11600 (2021).
Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. v. Self-training with Noisy Student improves ImageNet classification. arXiv:1911.04252 (2019).
He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. arXiv:1512.03385 (2015).
He, K., Zhang, X., Ren, S. & Sun, J. Identity Mappings in Deep Residual Networks. arXiv:1603.05027 (2016).
ダウンロード
公開済
投稿日時: 2022-09-20 04:54:45 UTC
公開日時: 2022-09-27 00:12:15 UTC — 2022-12-12 05:29:43 UTCに更新
バージョン
改版理由
考察に補足事項を追記しました。 その他、説明が不十分な点を追記しました。 英語のタイトル及びabstractを一部修正しました。ライセンス
Copyright（c）2022
古山, 誠
この作品は、Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licenseの下でライセンスされています。
