Abstract
Özet
Amaçlar: Meme kanseri, kadınlarda en yaygın görülen kanser türüdür ve kanserle ilgili ölümlerin büyük bir bölümünü oluşturur. Diğer kanser türlerinde olduğu gibi, meme kanserinin önlenmesi ve erken teşhisi gün geçtikçe daha da önem kazanmaktadır. Bu amaçla, yapay zeka tabanlı karar destek sistemleri son yıllarda popüler hale gelmektedir. Bu çalışmada, manyetik rezonans görüntüleme (MRG) protokolü ile çekilen görüntülerde meme lezyonlarını tespit etmek için otomatik bir meme lezyonu segmentasyon süreci önerilmektedir.
Yöntemler: İki en popüler segmentasyon yöntemi olan beklenti maksimizasyonu (EM) ve K-ortalama algoritmaları, meme lezyonlarının bölgesini belirlemek için kullanılmıştır. Ayrıca, EM ve K-ortalama yöntemlerinden sonra süper piksel tabanlı bulanık C-ortalama (SPFCM) algoritması, lezyon segmentasyon performansını artırmak için uygulanmıştır.
Sonuçlar: Önerilen yöntemler, yazarlar tarafından etik izinle oluşturulan özel bir veritabanında değerlendirilmiştir. Kullanılan yöntemlerin performansları, bir radyolog tarafından belirlenen lezyon alanlarıyla (gerçek veri) otomatik segmentasyon algoritmalarıyla elde edilen alanların karşılaştırılmasıyla analiz edilmiştir.
Sonuç: Performans karşılaştırması için Dice katsayısı, Jaccard endeksi (JI) ve eğri altı alan (AUC) ölçüleri hesaplanmıştır. Simülasyon sonuçlarına göre, EM, K-ortalama, EM+SPFCM ve K-ortalama+SPFCM yöntemleri meme MRG veritabanında iyi segmentasyon performansı sağlamaktadır. En iyi segmentasyon sonuçları EM+SPFCM hibrit yöntemi kullanılarak elde edilmektedir.
References
- Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3): 209-249.
- American Cancer Society. Breast MRI Scans. [Internet]. Available from: https://www.cancer.org/cancer/breastcancer/screening-tests-and-early-detection/breast-mri-scans.html.
- Pandey D, Yin X, Wang H, et al. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon. 2018;4(12):e01042.
- Illan IA, Ramirez J, Gorriz JM. Automated detection and segmentation of nonmass-enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging. Contrast Media Mol Imaging. 2018;2018:1-11. doi: 10.1155/2018/5308517
- Shokouhi SB, Fooladivanda A, Ahmadinejad N. Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter. Eurasip J Adv Signal Process. 2017;2017:1-11. doi: 10.1186/s13634-017-0476-x
- Marrone S, Piantadosi G, Fusco R, Petrillo A, Sansone M, Sansone C. Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI. Proc Int Conf Pattern Recognition. 2016:1472-1477. doi: 10.1109/ICPR.2016.7899845
- D’Amico NC, Grossi E, Valbusa G, et al. A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. Eur Radiol Exp. 2020;4(5):1-8. doi: 10.1186/s41747-019-0131-4
- Whitney M, Li H, Ji Y, Liu P, Giger ML. Comparison of breast MRI tumor classification using human-engineered radiomics, transfer learning from deep convolutional neural networks, and fusion methods. Proc IEEE. 2020;108:163-177. doi: 10.1109/JPROC.2019.2950187
- Guo YY, Huang YH, Wang Y, Huang J, Lai QQ, Li YZ. Breast MRI tumor automatic segmentation and triple-negative breast cancer discrimination algorithm based on deep learning. Comput Math Methods Med. 2022:2541358. doi.org/10.1155/2022/2541358
- Yue W, Zhang H, Zhou J, et al. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging. Front Oncol. 2022;12:984626. doi: 10. 3389/fonc.2022.984626
- Haq IU, Ali H, Wang HY, Cui L, Feng J. BTS-GAN: computer-aided segmentation system for breast tumor using MRI and conditional adversarial networks. Eng Sci Technol Int J. 2022;36:101154.
- Moon TK. The expectation-maximization algorithm. IEEE Signal Process Mag. 1996;13(6):47-60.
- Moftah HM, Azar AT, Al-Shammari ET, et al. Adaptive K-means clustering algorithm for MR breast image segmentation. Neural Comput & Applic. 2014;24:1917-1928. doi: 10.1007/s00521-013-1437-4
- Lei T, Jia X, Zhang Y, Liu S, Meng H, Nandi AK. Superpixel-based fast fuzzy C-Means clustering for color image segmentation. IEEE Trans Fuzzy Syst. 2019;27(9):1753-1766. doi: 10.1109/TFUZZ.2018. 2889018.
Abstract
Aims: Breast cancer is the most common type of cancer in women and accounts for a large portion of cancer-related deaths. As in the other types of cancer, the prevention and early diagnosis of breast cancer gain importance day after day. For this purpose, the artificial intelligence-based decision support systems become popular in recent years. In this study, an automatic breast lesion segmentation process is proposed to detect breast lesions in the images taken with magnetic resonance imaging (MRI) protocol.
Methods: Two most popular segmentation methods: expectation maximization (EM) and K-means algorithms are used to determine the region of breast lesions. Furthermore, superpixel based fuzzy C-means (SPFCM) algorithm is applied after EM and K-means methods to improve the lesion segmentation performance.
Results: The proposed methods are evaluated on the private database constructed by the authors with ethical permission. The performances of the utilized methods are analyzed by comparing the lesion areas determined by a radiologist (ground-truth) and areas that are achieved by automatic segmentation algorithms.
Conclusion: Dice coefficient, Jaccard index (JI), and area under curve (AUC) metrics are calculated for performance comparison. According to the simulation results, EM, K-means, EM+SPFCM, and K-means+SPFCM methods provides good segmentation performance on breast MRI database. The best segmentation results are obtained by using EM+SPFCM hybrid method. The results of the EM+SPFCM method are 0,8711, 0,8979, and 0,9981 for JI, Dice, and AUC, respectively.
Ethical Statement
The study was initiated with the approval of the Sakarya University Clinical Researches Ethics Committee (Date: 2016, Decision No: 17933).
References
- Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3): 209-249.
- American Cancer Society. Breast MRI Scans. [Internet]. Available from: https://www.cancer.org/cancer/breastcancer/screening-tests-and-early-detection/breast-mri-scans.html.
- Pandey D, Yin X, Wang H, et al. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon. 2018;4(12):e01042.
- Illan IA, Ramirez J, Gorriz JM. Automated detection and segmentation of nonmass-enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging. Contrast Media Mol Imaging. 2018;2018:1-11. doi: 10.1155/2018/5308517
- Shokouhi SB, Fooladivanda A, Ahmadinejad N. Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter. Eurasip J Adv Signal Process. 2017;2017:1-11. doi: 10.1186/s13634-017-0476-x
- Marrone S, Piantadosi G, Fusco R, Petrillo A, Sansone M, Sansone C. Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI. Proc Int Conf Pattern Recognition. 2016:1472-1477. doi: 10.1109/ICPR.2016.7899845
- D’Amico NC, Grossi E, Valbusa G, et al. A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. Eur Radiol Exp. 2020;4(5):1-8. doi: 10.1186/s41747-019-0131-4
- Whitney M, Li H, Ji Y, Liu P, Giger ML. Comparison of breast MRI tumor classification using human-engineered radiomics, transfer learning from deep convolutional neural networks, and fusion methods. Proc IEEE. 2020;108:163-177. doi: 10.1109/JPROC.2019.2950187
- Guo YY, Huang YH, Wang Y, Huang J, Lai QQ, Li YZ. Breast MRI tumor automatic segmentation and triple-negative breast cancer discrimination algorithm based on deep learning. Comput Math Methods Med. 2022:2541358. doi.org/10.1155/2022/2541358
- Yue W, Zhang H, Zhou J, et al. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging. Front Oncol. 2022;12:984626. doi: 10. 3389/fonc.2022.984626
- Haq IU, Ali H, Wang HY, Cui L, Feng J. BTS-GAN: computer-aided segmentation system for breast tumor using MRI and conditional adversarial networks. Eng Sci Technol Int J. 2022;36:101154.
- Moon TK. The expectation-maximization algorithm. IEEE Signal Process Mag. 1996;13(6):47-60.
- Moftah HM, Azar AT, Al-Shammari ET, et al. Adaptive K-means clustering algorithm for MR breast image segmentation. Neural Comput & Applic. 2014;24:1917-1928. doi: 10.1007/s00521-013-1437-4
- Lei T, Jia X, Zhang Y, Liu S, Meng H, Nandi AK. Superpixel-based fast fuzzy C-Means clustering for color image segmentation. IEEE Trans Fuzzy Syst. 2019;27(9):1753-1766. doi: 10.1109/TFUZZ.2018. 2889018.
Details
| Primary Language | English |
|---|---|
| Subjects | Computing Applications in Health, Radiology and Organ Imaging |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | October 26, 2023 |
| Publication Date | October 27, 2023 |
| Published in Issue | Year 2023 Volume: 5 Issue: 4 |
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