A pre-processing tool to increase performance of deep learning-based CAD in digital breast Tomosynthesis

Breast Cancer | Breast Cancer Information & Overview. https://www.cancer.org/cancer/breast-cancer.html. Accessed 30 Aug 2022.

Breast Cancer - Statistics. Cancer.Net. 2012. https://www.cancer.net/cancer-types/breast-cancer/statistics.Accessed 30 Aug 2022.

Google Scholar 

Chong A, Weinstein SP, McDonald ES, Conant EF. Digital breast Tomosynthesis: concepts and clinical practice. Radiology. 2019;292:1–14. https://doi.org/10.1148/radiol.2019180760.

Article  Google Scholar 

Tirada N, Li G, Dreizin D, Robinson L, Khorjekar G, Dromi S, Ernst T. Digital breast Tomosynthesis: physics, artifacts, and quality control considerations. RadioGraphics. 2019;39:413–26. https://doi.org/10.1148/rg.2019180046.

Article  Google Scholar 

Wei J, Chan H-P, Helvie MA, Roubidoux MA, Neal CH, Lu Y, Hadjiiski LM, Zhou C. Synthesizing mammogram from digital breast Tomosynthesis. Phys Med Biol. 2019;64:045011. https://doi.org/10.1088/1361-6560/aafcda.

Article  Google Scholar 

Ganesan K, Acharya UR, Chua KC, Min LC, Abraham KT. Pectoral muscle segmentation: a review. Comput Methods Prog Biomed. 2013;110:48–57. https://doi.org/10.1016/j.cmpb.2012.10.020.

Article  Google Scholar 

Ricciardi R, Mettivier G, Staffa M, Sarno A, Acampora G, Minelli S, Santoro A, Antignani E, Orientale A, Pilotti IAM, Santangelo V, D’Andria P, Russo P. A deep learning classifier for digital breast tomosynthesis. Phys Med. 2021;83:184–93. https://doi.org/10.1016/j.ejmp.2021.03.021.

Article  Google Scholar 

Ciatto S, Del Turco MR, Risso G, Catarzi S, Bonardi R, Viterbo V, Gnutti P, Guglielmoni B, Pinelli L, Pandiscia A, Navarra F, Lauria A, Palmiero R, Indovina PL. Comparison of standard reading and computer aided detection (CAD) on a national proficiency test of screening mammography. Eur J Radiol. 2003;45:135–8. https://doi.org/10.1016/s0720-048x(02)00011-6.

Article  Google Scholar 

Taghanaki SA, Liu Y, Miles B, Hamarneh G. Geometry-based pectoral muscle segmentation from MLO mammogram views. IEEE Trans Biomed Eng. 2017;64:2662–71. https://doi.org/10.1109/TBME.2017.2649481.

Article  Google Scholar 

Tavakoli N, Karimi M, Norouzi A, Karimi N, Samavi S, Soroushmehr SMR. Detection of abnormalities in mammograms using deep features. J Ambient Intell Human Comput. 2019. https://doi.org/10.1007/s12652-019-01639-x.

Ali MJ, Raza B, Shahid AR, Mahmood F, Yousuf MA, Dar AH, Iqbal U. Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network. Int J Imaging Syst Technol. 2020;30:1108–18. https://doi.org/10.1002/ima.22410.

Article  Google Scholar 

Saltanat N, Hossain MA, Alam MS (2010) An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms. In: 2010 IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA). Pp 1510–1517.

Pertuz S, Torres GF, Tamimi R, Kämäräinen J. Open framework for mammography-based breast cancer risk assessment. In: 2019 IEEE EMBS international conference on biomedical and health informatics, BHI 2019 - proceedings. IEEE; 2019.

Google Scholar 

Vagssa P, Doudou NM, Jolivo T, Videme O, Kolyang DT. Pectoral muscle deletion on a mammogram to aid in the early diagnosis of breast cancer. Int J Eng Sci Technol. 2020;12:57–65. https://doi.org/10.4314/ijest.v12i3.6.

Article  Google Scholar 

Beeravolu AR, Azam S, Jonkman M, Shanmugam B, Kannoorpatti K, Anwar A. Preprocessing of breast Cancer images to create datasets for deep-CNN. IEEE Access. 2021;9:33438–63. https://doi.org/10.1109/ACCESS.2021.3058773.

Article  Google Scholar 

Xu W, Li L, Liu W (2007) A novel pectoral muscle segmentation algorithm based on polyline fitting and elastic thread approaching. In: 2007 1st international conference on bioinformatics and biomedical engineering. Pp 837–840.

Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frère AF. Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging. 2004;23:232–45. https://doi.org/10.1109/tmi.2003.823062.

Article  Google Scholar 

Yu X, Wang S-H, Górriz JM, Jiang X-W, Guttery DS, Zhang Y-D. PeMNet for Pectoral Muscle Segmentation. Biology. 2022;11:134. https://doi.org/10.3390/biology11010134.

Article  Google Scholar 

Soleimani H, Michailovich OV. On segmentation of pectoral muscle in digital mammograms by means of deep learning. IEEE Access. 2020;8:204173–82. https://doi.org/10.1109/ACCESS.2020.3036662.

Article  Google Scholar 

Feudjio CK, Tiedeu A, Noubeg M-L, Gordan M, Vlaicu A, Domngang S. Extracting and smoothing contours in mammograms using Fourier descriptors. J Biomed Sci Eng. 2014;2014 https://doi.org/10.4236/jbise.2014.73017.

Martí R, Oliver A, Raba D, Freixenet J. Breast skin-line segmentation using contour growing. In: Martí J, Benedí JM, Mendonça AM, Serrat J, editors. Pattern recognition and image analysis. Berlin, Heidelberg: Springer; 2007. p. 564–71.

Chapter  Google Scholar 

Silva CA, Lima CG, Correia JH (2011) Breast skin-line detection using dynamic programming. In: 2011 annual international conference of the IEEE engineering in medicine and biology society. Pp 7775–7778.

Jen C-C, Yu S-S. Automatic nipple detection in mammograms using local maximum features along breast contour. Biomed Eng Appl Basis Commun. 2015;27:1550035. https://doi.org/10.4015/S1016237215500350.

Article  Google Scholar 

Mettivier G, Ricciarci R, Sarno A, Maddaloni FS, Porzio M, Staffa M, Minelli S, Santoro A, Antignani E, Masi M, Landoni V, Ordonez P, Ferranti F, Greco L, Clemente S, Russo P. DeepLook: a deep learning computed diagnosis support for breast tomosynthesis. In: 16th international workshop on breast imaging (IWBI2022). SPIE; 2022. p. 161–8.

Google Scholar 

Langarizadeh M, Mahmud R, Ramli AR, Napis S, Beikzadeh MR, Rahman WEZWA. Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter. J Med Eng Technol. 2011;35:103–8. https://doi.org/10.3109/03091902.2010.542271.

Article  Google Scholar 

Li X, Jiao H, Wang Y. Edge detection algorithm of cancer image based on deep learning. Bioengineered. 2020;11:693–707. https://doi.org/10.1080/21655979.2020.1778913.

Article  Google Scholar 

Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, Lo JY, Mazurowski MA. A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast Tomosynthesis images. JAMA Netw Open. 2021;4:e2119100. https://doi.org/10.1001/jamanetworkopen.2021.19100.

Article  Google Scholar 

Paris S, Kornprobst P, Tumblin J, Durand F A Gentle Introduction to Bilateral Filtering and its Applications. 130.

Andreozzi E, Fratini A, Esposito D, Cesarelli M, Bifulco P. Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devices. Biomed Eng Online. 2021;20 https://doi.org/10.1186/s12938-021-00874-8.

Santos CFGD, Papa JP. Avoiding overfitting: a survey on regularization methods for convolutional neural networks. ACM Comput Surv. 2022;54:213:1-213:25. https://doi.org/10.1145/3510413.

Article  Google Scholar 

Aggarwal CC. Neural networks and deep learning: a textbook. Cham: Springer International Publishing; 2018.

Book  MATH  Google Scholar 

Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Mujtaba G, Nweke HF, Al-garadi MA, Zulfiqar F, Raza G, Azmi NA. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev. 2020;53:1655–720. https://doi.org/10.1007/s10462-019-09716-5.

Article  Google Scholar 

Koshy SS, Anbarasi LJ, Jawahar M, Ravi V. Breast cancer image analysis using deep learning techniques – a survey. Health Technol. 2022;12:1133–55. https://doi.org/10.1007/s12553-022-00703-5.

Article  Google Scholar 

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