Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications

Michael, E., Ma, H., Li, H., Kulwa, F., Li, J.: Breast cancer segmentation methods: current status and future potentials. BioMed Research International. 2021, 1–29 (2021)

Article  Google Scholar 

Saffari, N., Rashwan, H.A., Abdel-Nasser, M., Kumar Singh, V., Arenas, M., Mangina, E., Herrera, B., Puig, D.: Fully automated breast density segmentation and classification using deep learning. Diagnostics. 10(11), 988 (2020)

Article  PubMed  PubMed Central  Google Scholar 

Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R.: A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 292(1), 60–66 (2019)

Article  PubMed  Google Scholar 

Rummel, S., Hueman, M.T., Costantino, N., Shriver, C.D., Ellsworth, R.E.: Tumour location within the breast: Does tumour site have prognostic ability? Ecancermedicalscience. 9 (2015)

Kestelman, F., Gomes, C., Fontes, F., Marchiori, E.: Imaging findings of papillary breast lesions: a pictorial review. Clinical radiology. 69(4), 436–441 (2014)

Article  CAS  PubMed  Google Scholar 

Youk, J.H., Kim, E.-K., Kim, M.J., Oh, K.K.: Imaging findings of chest wall lesions on breast sonography. Journal of Ultrasound in Medicine. 27(1), 125–138 (2008)

Article  PubMed  Google Scholar 

Lee, C.H., Dershaw, D.D., Kopans, D., Evans, P., Monsees, B., Monticciolo, D., Brenner, R.J., Bassett, L., Berg, W., Feig, S., et al.: Breast cancer screening with imaging: recommendations from the society of breast imaging and the acr on the use of mammography, breast mri, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. Journal of the American college of radiology. 7(1), 18–27 (2010)

Article  PubMed  Google Scholar 

Perry, N., Broeders, M., Wolf, C., Törnberg, S., Holland, R., Karsa, L.: European guidelines for quality assurance in breast cancer screening and diagnosis. -summary document. Oncology in Clinical Practice. 4(2), 74–86 (2008)

Mustra, M., Grgic, M.: Robust automatic breast and pectoral muscle segmentation from scanned mammograms. Signal processing. 93(10), 2817–2827 (2013)

Article  Google Scholar 

Liu, L., Liu, Q., Lu, W.: Pectoral muscle detection in mammograms using local statistical features. Journal of digital imaging. 27, 633–641 (2014)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Oliver, A., Lladó, X., Torrent, A., Martí, J.: One-shot segmentation of breast, pectoral muscle, and background in digitised mammograms. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 912–916 (2014). IEEE

Sreedevi, S., Sherly, E.: A novel approach for removal of pectoral muscles in digital mammogram. Procedia Computer Science. 46, 1724–1731 (2015)

Article  Google Scholar 

Taghanaki, S.A., Liu, Y., Miles, B., Hamarneh, G.: Geometry-based pectoral muscle segmentation from mlo mammogram views. IEEE Transactions on Biomedical Engineering. 64(11), 2662–2671 (2017)

Article  PubMed  Google Scholar 

Vikhe, P., Thool, V.: Detection and segmentation of pectoral muscle on mlo-view mammogram using enhancement filter. Journal of medical systems. 41, 1–13 (2017)

Article  Google Scholar 

Rampun, A., Morrow, P.J., Scotney, B.W., Winder, J.: Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artificial intelligence in medicine. 79, 28–41 (2017)

Article  PubMed  Google Scholar 

Hazarika, M., Mahanta, L.B.: A novel region growing based method to remove pectoral muscle from mlo mammogram images. In: Advances in Electronics, Communication and Computing: ETAEERE-2016, pp. 307–316 (2018). Springer

Toz, G., Erdogmus, P.: A single sided edge marking method for detecting pectoral muscle in digital mammograms. Engineering, Technology and Applied Science Research. 8(1), 2367–2373 (2018)

Article  Google Scholar 

Ahmed, L., Iqbal, M.M., Aldabbas, H., Khalid, S., Saleem, Y., Saeed, S.: Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 1–17 (2020)

Divyashree, B., Amarnath, R., Naveen, M., Kumar, H.: Segmentation of pectoral muscle in mammograms using granular computing. Journal of Information Technology Research (JITR). 15(1), 1–14 (2022)

Article  Google Scholar 

Rampun, A., López-Linares, K., Morrow, P.J., Scotney, B.W., Wang, H., Ocaña, I.G., Maclair, G., Zwiggelaar, R., Ballester, M.A.G., Macía, I.: Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Medical image analysis. 57, 1–17 (2019)

Article  PubMed  Google Scholar 

Soleimani, H., Michailovich, O.V.: On segmentation of pectoral muscle in digital mammograms by means of deep learning. IEEE Access. 8, 204173–204182 (2020)

Article  Google Scholar 

Ali, M.J., Raza, B., Shahid, A.R., Mahmood, F., Yousuf, M.A., Dar, A.H., Iqbal, U.: Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network. International Journal of Imaging Systems and Technology. 30(4), 1108–1118 (2020)

Article  Google Scholar 

Guo, Y., Zhao, W., Li, S., Zhang, Y., Lu, Y.: Automatic segmentation of the pectoral muscle based on boundary identification and shape prediction. Physics in Medicine & Biology. 65(4), 045016 (2020)

Article  Google Scholar 

Rubio, Y., Montiel, O.: Multicriteria evaluation of deep neural networks for semantic segmentation of mammographies. Axioms. 10(3), 180 (2021)

Article  Google Scholar 

Yu, X., Wang, S.-H., Górriz, J.M., Jiang, X.-W., Guttery, D.S., Zhang, Y.-D.: Pemnet for pectoral muscle segmentation. Biology. 11(1), 134 (2022)

PubMed  Google Scholar 

Verboom, S.D., Caballo, M., Peters, J., Gommers, J., Oever, D., Broeders, M.J., Teuwen, J., Sechopoulos, I.: Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors. Journal of Medical Imaging. 11(1), 014001–014001 (2024)

PubMed  Google Scholar 

Silva, S.V., Sierra-Franco, C.A., Hurtado, J., Cruz, L.C., Thomaz, V.d.A., Silva-Calpa, G.F.M., Raposo, A.B.: A data-centric approach for pectoral muscle deep learning segmentation enhancements in mammography images. In: International Symposium on Visual Computing, pp. 56–67 (2023). Springer

Ge, M., Mawdsley, G., Yaffe, M.: Automatic identification of pectoralis muscle on digital cranio-caudal-view mammograms. In: Medical Imaging 2011: Computer-Aided Diagnosis, vol. 7963, pp. 572–579 (2011). SPIE

Ge, M., Mainprize, J.G., Mawdsley, G.E., Yaffe, M.J.: Segmenting pectoralis muscle on digital mammograms by a markov random field-maximum a posteriori model. Journal of Medical Imaging. 1(3), 034503–034503 (2014)

Article  PubMed  PubMed Central  Google Scholar 

Yin, F.-F., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A.: Computerized detection of masses in digital mammograms: Automated alignment of breast images and its effect on bilateral-subtraction technique. Medical Physics. 21(3), 445–452 (1994)

Article  CAS  PubMed  Google Scholar 

Méndez, A.J., Tahoces, P.G., Lado, M.J., Souto, M., Correa, J., Vidal, J.J.: Automatic detection of breast border and nipple in digital mammograms. Computer methods and programs in biomedicine. 49(3), 253–262 (1996)

Article  PubMed  Google Scholar 

Chandrasekhar, R., Attikiouzel, Y.: A simple method for automatically locating the nipple on mammograms. IEEE transactions on medical imaging. 16(5), 483–494 (1997)

Article  CAS  PubMed  Google Scholar 

Mustra, M., Bozek, J., Grgic, M.: Nipple detection in craniocaudal digital mammograms. In: 2009 International Symposium ELMAR, pp. 15–18 (2009). IEEE

Zhou, C., Chan, H.-P., Paramagul, C., Roubidoux, M.A., Sahiner, B., Hadjiiski, L.M., Petrick, N.: Computerized nipple identification for multiple image analysis in computer-aided diagnosis: Computerized nipple identification on mammograms. Medical Physics. 31(10), 2871–2882 (2004)

Article  PubMed  Google Scholar 

Kinoshita, S.K., Azevedo-Marques, P.M., Pereira, R.R., Rodrigues, J.A.H., Rangayyan, R.M.: Radon-domain detection of the nipple and the pectoral muscle in mammograms. Journal of digital imaging. 21, 37–49 (2008)

Article  CAS  PubMed  Google Scholar 

Casti, P., Mencattini, A., Salmeri, M., Ancona, A., Mangieri, F.F., Pepe, M.L., Rangayyan, R.M.: Automatic detection of the nipple in screen-film and full-field digital mammograms using a novel hessian-based method. Journal of digital imaging. 26, 948–957 (2013)

Article  PubMed  PubMed Central  Google Scholar 

Jiang, J., Zhang, Y., Lu, Y., Guo, Y., Chen, H.: A radiomic feature–based nipple detection algorithm on digital mammography. Medical physics. 46(10), 4381–4391 (2019)

Article  PubMed  Google Scholar 

Lin, Y., Li, M., Chen, S., Yu, L., Ma, F.: Nipple detection in mammogram using a new convolutional neural network architecture. In: 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6 (2019). IEEE

He, W., Juette, A., Denton, E.R., Oliver, A., Martí, R., Zwiggelaar, R., et al.: A review on automatic mammographic density and parenchymal segmentation. International journal of breast cancer. 2015 (2015)

Matsubara, T., Yamazaki, D., Kato, M., Hara, T., Fujita, H., Iwase, T., Endo, T.: An automated classification scheme for mammograms based on amount and distribution of fibroglandular breast tissue density. In: International Congress Series, vol. 1230, pp. 545–552 (2001). Elsevier

El-Zaart, A.: Expectation–maximization technique for fibro-glandular discs detection in mammography images. Computers in Biology and Medicine. 40(4), 392–401 (2010)

Article  PubMed  Google Scholar 

Highnam, R., Brady, S.M., Yaffe, M.J., Karssemeijer, N., Harvey, J.: Robust breast composition measurement-volpara tm. In: Digital Mammography: 10th International Workshop, IWDM 2010, Girona, Catalonia, Spain, June 16-18, 2010. Proceedings 10, pp. 342–349 (2010). Springer

Torres, G.F., Sassi, A., Arponen, O., Holli-Helenius, K., Lääperi, A.-L., Rinta-Kiikka, I., Kämäräinen, J., Pertuz, S.: Morphological area gradient: System-independent dense tissue segmentation in mammography images. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4855–4858 (2019). IEEE

Keller, B.M., Nathan, D.L., Wang, Y., Zheng, Y., Gee, J.C., Conant, E.F., Kontos, D.: Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Medical physics. 39(8), 4903–4917 (2012)

Article  PubMed  PubMed Central  Google Scholar 

Keller, B.M., Chen, J., Daye, D., Conant, E.F., Kontos, D.: Preliminary evaluation of the publicly available laboratory for breast radiodensity assessment (libra) software tool: comparison of fully automated area and volumetric density measures in a case–control study with digital mammography. Breast cancer research. 17, 1–17 (2015)

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif