Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Yıl 2023, , 871 - 886, 28.09.2023

Oğuzhan Katar , Özal Yıldırım

https://doi.org/10.17798/bitlisfen.1331310

Öz

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

Anahtar Kelimeler

Breast cancer, Ultrasound images, Semantic segmentation, U-Net, Disease detection, Deep learning.

Kaynakça

• [1] J. S. You and P. A. Jones, “Cancer genetics and epigenetics: two sides of the same coin?,” Cancer Cell, vol. 22, no. 1, pp. 9–20, 2012.
• [2] S. Gómez-López, R. G. Lerner, and C. Petritsch, “Asymmetric cell division of stem and progenitor cells during homeostasis and cancer,” Cellular and Molecular Life Sciences, vol. 71, pp. 575–597, 2014.
• [3] N. Parsa, “Environmental factors inducing human cancers,” Iran J Public Health, vol. 41, no. 11, p. 1, 2012. [4] M. Hejmadi, Introduction to cancer biology. Bookboon, 2014.
• [5] M. Amrane, S. Oukid, I. Gagaoua, and T. Ensari, “Breast cancer classification using machine learning,” in 2018 electric electronics, computer science, biomedical engineerings’ meeting (EBBT), IEEE, 2018, pp. 1–4.
• [6] J. Boutry et al., “The evolution and ecology of benign tumors,” Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, vol. 1877, no. 1, p. 188643, 2022.
• [7] K. Soda, “The mechanisms by which polyamines accelerate tumor spread,” Journal of Experimental & Clinical Cancer Research, vol. 30, pp. 1–9, 2011.
• [8] L. Wilkinson and T. Gathani, “Understanding breast cancer as a global health concern,” Br J Radiol, vol. 95, no. 1130, p. 20211033, 2022.
• [9] K. Mortezaee, “Organ tropism in solid tumor metastasis: an updated review,” Future Oncology, vol. 17, no. 15, pp. 1943–1961, 2021.
• [10] O. Ginsburg et al., “Breast cancer early detection: A phased approach to implementation,” Cancer, vol. 126, pp. 2379–2393, 2020.
• [11] E. Michael, H. Ma, H. Li, and S. Qi, “An optimized framework for breast cancer classification using machine learning,” Biomed Res Int, vol. 2022, 2022.
• [12] F. A. González-Luna, J. Hernández-López, and W. Gomez-Flores, “A performance evaluation of machine learning techniques for breast ultrasound classification,” in 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 2019, pp. 1–5.
• [13] M. Wei, Y. Du, X. Wu, and J. Zhu, “Automatic classification of benign and malignant breast tumors in ultrasound image with texture and morphological features,” in 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), IEEE, 2019, pp. 126–130.
• [14] K. Atrey, B. K. Singh, N. K. Bodhey, and R. B. Pachori, “Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach,” Biomed Signal Process Control, vol. 86, p. 104919, 2023.
• [15] A. Raza, N. Ullah, J. A. Khan, M. Assam, A. Guzzo, and H. Aljuaid, “DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images,” Applied Sciences, vol. 13, no. 4, p. 2082, 2023.
• [16] S. Gupta, S. Agrawal, S. K. Singh, and S. Kumar, “A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification,” in Computational Vision and Bio-Inspired Computing: Proceedings of ICCVBIC 2022, Springer, 2023, pp. 511–523.
• [17] M. Byra et al., “Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network,” Biomed Signal Process Control, vol. 61, p. 102027, 2020.
• [18] S. R. Sannasi Chakravarthy and H. Rajaguru, “SKMAT‐U‐Net architecture for breast mass segmentation,” Int J Imaging Syst Technol, vol. 32, no. 6, pp. 1880–1888, 2022.
• [19] W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data Brief, vol. 28, p. 104863, 2020.
• [20] F. Oztekin et al., “Automatic semantic segmentation for dental restorations in panoramic radiography images using U‐Net model,” Int J Imaging Syst Technol, vol. 32, no. 6, pp. 1990–2001, 2022.
• [21] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, 2015, pp. 234–241.
• [22] J. Zhang, Y. Zhang, Y. Jin, J. Xu, and X. Xu, “MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation,” Health Inf Sci Syst, vol. 11, no. 1, p. 13, 2023.
• [23] A. Abedalla, M. Abdullah, M. Al-Ayyoub, and E. Benkhelifa, “Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures,” PeerJ Comput Sci, vol. 7, p. e607, 2021.
• [24] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition, IEEE, 2009, pp. 248–255.
• [25] X. Zhong and H. Ban, “Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem,” Ann Nucl Energy, vol. 175, p. 109201, 2022.
• [26] M. H. BENDIABDALLAH and N. SETTOUTI, “A comparison of U-net backbone architectures for the automatic white blood cells segmentation,” WAS Science Nature, vol. 4, no. 1, 2021.
• [27] A. Abedalla, M. Abdullah, M. Al-Ayyoub, and E. Benkhelifa, “The 2ST-UNet for pneumothorax segmentation in chest X-Rays using ResNet34 as a backbone for U-Net,” arXiv preprint arXiv:2009.02805, 2020.
• [28] M. Xi, J. Li, Z. He, M. Yu, and F. Qin, “NRN-RSSEG: A deep neural network model for combating label noise in semantic segmentation of remote sensing images,” Remote Sens, vol. 15, no. 1, p. 108, 2022.
• [29] A. N. Gajjar and J. Jethva, “Intersection over Union based analysis of Image detection/segmentation using CNN model,” in 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T), IEEE, 2022, pp. 1–6.
• [30] A. M. H. Mahran, W. Hussein, and S. E. D. M. Saber, “Automatic Teeth Segmentation Using Attention U-Net,” Preprint, 2023.
• [31] H. Kai, Z. Y. Feng, H. Meng, F. Y. Baoping, and Y. R. Han, “Ultrasound Image Segmentation of Breast Tumors Based on Swin-transformerv2,” in Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City, 2022, pp. 106–111.
• [32] M. S. K. Inan, F. I. Alam, and R. Hasan, “Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images,” Biomed Signal Process Control, vol. 75, p. 103553, 2022.
• [33] M. Bal-Ghaoui, M. H. E. Y. Alaoui, A. Jilbab, and A. Bourouhou, “U-Net transfer learning backbones for lesions segmentation in breast ultrasound images,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 5, pp. 5747–5754, 2023.
• [34] K. Azam, M. A. Azam, M. A. Qureshi, K. B. Khan, and M. A. Azam, “Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation,” in 2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T), IEEE, 2023, pp. 1–6.