Augmentation of Image Sets for Training Neural Networks in Solving Semantic Segmentation Problems

I.A. Lozhkin, M.E. Dunaev, K.S. Zaytsev, A.A. Garmash

Abstract


The purpose of this work is to study the effectiveness of augmentation methods of image sets when they are insufficient in training sample of neural networks for solving semantic segmentation problems. For this purpose, the main groups of augmentation methods were considered and their effectiveness in solving problems of semantic segmentation of medical images was investigated. Two deep architectures DeepLabV3+ with the EfficientNetB6 encoder were used for training, testing and validation. The Intersection over Union and Dice coefficient were chosen as the target metrics for comparing the quality of semantic segmentation of images, which made it possible to determine the models with the best predictions. The obtained results confirmed the effectiveness of the proposed set of augmentation methods. The result of the work was the creation of an effective approach to augmentation of medical image sets to solve the problem of semantic segmentation.

Full Text:

PDF (Russian)

References


Deo R.C. Machine Learning in Medicine // Circulation. Vol. 132. 20. 2015. P. 1920-1930.

Acs B., Rantalainen M., Hartman J. Artificial intelligence as the next step towards precision pathology // Journal of Internal Medicine. 2020. 288. P. 62-81.

Lipkova J., Chen R.J., Chen B., Lu M.Y., Barbieri M., Shao D., Vaidya A.J., Chen C., Zhuang L., Williamson D.F.K., Shaban M., Chen T.Y., Mahmood F. Artificial intelligence for multimodal data integration in oncology. 2022. Vol. 40. P. 1095-1110.

Vanushko V.E. Thyroid nodules are not always pathology. InfoMedFarm Dialogue, 2022 [Internet source]. — Access mode: https://imfd.ru/2022/03/15/yzlishitzelez/, free — (27.10.2022).

Botz B. European Thyroid Association TIRADS, 2021 [Internet source]. — Access mode: https://radiopaedia.org/articles/european-thyroid-association-tirads, free — (27.10.2022).

Kang Q., Lao Q., Li Y., Jiang Z., Qiu Y., Zhang S., Li K. Thyroid nodule segmentation and classification in ultrasound images through intra- and inter-task consistent learning // Medical Image Analysis. 2022. Vol. 79.

Wang M., Yuan C., Wu D., Zeng Y., Zhong S., Qiu W.. Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks // MICCAI 2020: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. 2020. P. 109-115.

Maharana K., Mondal S., Nemade B. A review: Data pre-processing and data augmentation techniques // Global Transitions Proceedings. 2022. Vol. 3. P. 91-99.

Nalepa J., Marcinkiewicz M., Kawulok M. Data augmentation for brain-tumor segmentation: A review // Frontiers in Computational Neuroscience. 2019. Vol. 13.

Lee J., Liu C., Kim J., Chen Z., Sun Y., Rogers J.R., Chung W.K., Weng C. Deep Learning for Rare Disease: A Scoping Review // Journal of Biomedical Informatics. 2022. Vol. 135.

Zhou S., Nie D., Adeli E., Wei Q., Ren X., Liu X., Zhu E., Yin J., Wang Q., Shen D. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends // Medical Image Analysis. 2022. Vol. 82.

Chlap P., Min H., Vandenberg N., Dowling J., Holloway L., Haworth A. A review of medical image data augmentation techniques for deep learning applications // Journal of Medical Imaging and Radiation Oncology. 2021. Vol. 65, Issue 5. P. 545-563.

Hoar D., Lee P.Q., Guida A., Patterson S., Bowen C.V., Merrimen J., Wang C., Rendon R., Beyea S.D., Clarke S.E. Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images. 2021. V. 210.

Image Test Time Augmentation with PyTorch. TTAch [Internet source]. — Access mode: https://github.com/qubvel/ttach, free — (27.10.2022).

Library imgaug documentation for image augmentation [Internet source]. — Access mode: https://imgaug.readthedocs.io/en/latest/, free — (27.10.2022).

Hussain Z., Gimenez F., Yi D., Rubin D. Differential Data Augmentation Techniques for Medical Imaging Classification Tasks // AMIA Symposium. 2017. P. 979-984.

Chen Y., Yang X. H., Wei Z., Heidari A. A., Zheng N., Li Z., Chen H., Hu H., Zhou Q., Guan Q. Generative Adversarial Networks in Medical Image augmentation: A review // Computers in Biology and Medicine. 2022. Vol. 144.

Shi G., Wang J., Qiang Y., Yang X., Zhao J., Hao R., Yang W., Du Q., Kazihise N. G. Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification // Computer Methods and Programs in Biomedicine. 2020. Vol. 196.

Chen L., Zhu Y., Papandreou G., Schroff F., Adam H. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation // ECCV 2018. Computer Science, Computer Vision and Pattern Recognition. 2018.

Tan M., Le Q. V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks // ICML 2019. Machine Learning, Computer Vision and Pattern Recognition. 2019.


Refbacks

  • There are currently no refbacks.


Abava  Absolutech Convergent 2022

ISSN: 2307-8162