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Tytuł artykułu
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Warianty tytułu
Języki publikacji
Abstrakty
Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. X-ray mammogram analysis mainly refers to the localization of suspicious regions of interest followed by segmentation, towards further lesion classification into benign versus malignant. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. However, manually segmenting breast masses from native mammograms is time-consuming and error-prone. Therefore, an integrated computer-aided diagnosis system is required to assist clinicians for automatic and precise breast mass delineation. In this work, we present a two-stage multiscale pipeline that provides accurate mass contours from high-resolution full mammograms. First, we propose an extended deep detector integrating a multi-scale fusion strategy for automated mass localization. Second, a convolutional encoder-decoder network using nested and dense skip connections is employed to fine-delineate candidate masses. Unlike most previous studies based on segmentation from regions, our framework handles mass segmentation from native full mammograms without any user intervention. Trained on INbreast and DDSM-CBIS public datasets, the pipeline achieves an overall average Dice of 80.44% on INbreast test images, outperforming state-of-the-art. Our system shows promising accuracy as an automatic full-image mass segmentation system. Extensive experiments reveals robustness against the diversity of size, shape and appearance of breast masses, towards better interaction-free computer-aided diagnosis.
Wydawca
Czasopismo
Rocznik
Tom
Strony
746--757
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
autor
- Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France
autor
- Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France
autor
- Inserm, LaTIM UMR 1101, Brest, France
autor
- Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France
autor
- Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France; University Hospital of Brest, Brest, France
autor
- Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France
Bibliografia
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- [4] Myers ER, Moorman P, Gierisch JM, Havrilesky LJ, Grimm LJ, Ghate S, Davidson B, Mongtomery RC, Crowley MJ, McCrory DC, Kendrick A, Sanders GD. Benefits and harms of breast cancer screening: a systematic review. J Am Med Assoc 2015;314(15):1615–34.
- [5] Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. J Am Med Assoc Internal Med 2015;175(11):1828–37.
- [6] Li H, Chen D, Nailon WH, Davies ME, Laurenson D. Improved breast mass segmentation in mammograms with conditional residual u-net. Image Analysis for Moving Organ, Breast, and Thoracic Images 2018:81–9.
- [7] Singh VK, Rashwan HA, Romani S, Akram F, Pandey N, Sarker MMK, Saleh A, Arenas M, Arquez M, Puig D, et al. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl 2020;139.
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- [9] Yan Y, Conze P-H, Decencière E, Lamard M, Quellec G, Cochener B, Coatrieux G. Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in highresolution mammograms. IEEE International Engineering in Medicine and Biology 2019.
- [10] Yan Y, Conze P-H, Quellec G, Lamard M, Cochener B, Coatrieux G. Two-stage multi-scale mass segmentation from full mammograms. In IEEE International Symposium on Biomedical Imaging; 2021.
- [11] Hizukuri A, Nakayama R, Ashiba H. Segmentation method of breast masses on ultrasonographic images using level set method based on statistical model. J Biomed Sci Eng 2017;10 (4).
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- [13] Hmida M, Hamrouni K, Solaiman B, Boussetta S. An efficient method for breast mass segmentation and classification in mammographic images. Int J Adv Comput Sci Appl 2017;8 (11):256–62.
- [14] Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng 2020;40(1):290–305.
- [15] Dalwinder S, Birmohan S, Manpreet K. Simultaneous feature weighting and parameter determination of neural networks using ant lion optimization for the classification of breast cancer. Biocybern Biomed Eng 2020;40(1):337–51.
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- [22] Caballo M, Pangallo DR, Mann RM, Sechopoulos I. Deep learning-based segmentation of breast masses in dedicated breast ct imaging: Radiomic feature stability between radiologists and artificial intelligence. Comput Biol Med 2020;118.
- [23] Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M. Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed Signal Process Control 2020;61.
- [24] Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: a nested U-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; 2018. pp. 3–11.
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- [28] Girshick R, Donahue J, Darrell T, Malik J. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 2015;38 (1):142–58.
- [29] Agarwal R, Diaz O, Lladó X, Yap MH, Martı´ R. Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imag 2019;6(3).
- [30] Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S, Woo O, Kang J. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS One 2018;13(9).
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- [34] Dhungel N, Carneiro G, Bradley AP. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 2017;37:114–28.
- [35] Al-antari MA, Al-masni MA, Choi M-T, Han S-M, Kim T-S. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inf 2018;117:44–54.
- [36] Moreira IC, Amaral IF, Domingues I, Cardoso AJM, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol 2012;19(2):236–48.
- [37] Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data 2017.
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- [43] Conze P-H, Brochard S, Burdin V, Sheehan FT, Pons C. Healthy versus pathological learning transferability in shoulder muscle mri segmentation using deep convolutional encoderdecoders. Computerized Med Imag Graph 2020.
- [44] Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with deep learning. Scientific Rep 2018;8(1):4165.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-efbc927a-5ec4-4bac-8b56-0bcdd5789d71