Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention

• Autorzy: Yan Yutong , Conze Pierre-Henri , Quellec Gwenolé , Lamard Mathieu , Cochener Beatrice , Coatrieux Gouenou

Identyfikatory

DOI

10.1016/j.bbe.2021.03.005

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

breast cancer; X-ray mammogram; mass segmentation; multiscale detection; deep convolutional encoder-decoder; computer-aided diagnosis

PL

nowotwór piersi; mammografia; wykrywanie wieloskalowe; diagnoza wspomagana komputerowo

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 2
• Strony: 746--757
• Opis fizyczny: Bibliogr. 44 poz., rys., tab., wykr.

Twórcy

autor

Yan Yutong

• Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France

autor

Conze Pierre-Henri

• Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France

autor

Quellec Gwenolé

• Inserm, LaTIM UMR 1101, Brest, France

autor

Lamard Mathieu

• Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France

autor

Cochener Beatrice

• Université de Bretagne Occidentale, Brest, France; Inserm, LaTIM UMR 1101, Brest, France; University Hospital of Brest, Brest, France

autor

Coatrieux Gouenou

• Inserm, LaTIM UMR 1101, Brest, France; IMT Atlantique, Brest, France

Bibliografia

• [1] Torre LA, Islami F, Siegel RL, Ward EM, Jemal A. Global cancer in women: burden and trends, cancer epidemiology and prevention. Biomarkers 2017;26(4):444–57.
• [2] Singel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA: A Cancer J Clin 2018;68(1):7–30.
• [3] Virmani J, Agarwal R, et al. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019;39(2):536–60.
• [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.
• [8] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Med Image Comput Comput-Assisted Intervent 2015:234–41.
• [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).
• [12] Liu Y, Ren L, Cao X, Tong Y. Breast tumors recognition based on edge feature extraction using support vector machine. Biomed Signal Process Control 2020;58.
• [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.
• [16] Oliver A, Tortajada M, Lladó X, Freixenet J, Ganau S, Tortajada L, Vilagran M, Sentís M, Martí R. Breast density analysis using an automatic density segmentation algorithm. J Digital Imag 2015;28(5):604–12.
• [17] Kanbayti IH, Rae WI, McEntee MF, Al-Foheidi M, Ashour S, Turson SA, Ekpo EU. Is mammographic density a marker of breast cancer phenotypes? Cancer Causes & Control: CCC; 2020.
• [18] Skarping I, Förnvik D, Sartor H, Heide-Jørgensen U, Zackrisson S, Borgquist S. Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer. BMC Cancer 2019;19(1):1–11.
• [19] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Conference on Computer vision and Pattern Recognition 2015:3431–40.
• [20] Badrinarayanan V, Handa A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv preprint arXiv:1505.07293; 2015.
• [21] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. Advances in Neural Information Processing Systems 2014:2672–80.
• [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.
• [25] Redmon J, Farhadi A. YOLOv3: an incremental improvement, arXiv preprint arXiv:1804.02767; 2018.
• [26] Ren S, He K, Girshick R, Sun J, Faster R-CNN. Towards realtime object detection with region proposal networks. Advances in Neural Information Processing Systems 2015:91–9.
• [27] Dai J, Li Y, He K, Sun J. R-FCN: object detection via regionbased fully convolutional networks. Advances in Neural Information Processing Systems 2016:379–87.
• [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).
• [31] Lin T-Y, Goyal P, Girshick R, He K, Dolla´ r P. Focal loss for dense object detection. IEEE International Conference on Computer Vision 2017:2980–8.
• [32] Yap MH, Goyal M, Osman F, Marti R, Denton E, Juette A, Zwiggelaar R. Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med 2020.
• [33] Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning, arXiv preprint arXiv:1602.07261; 2016.
• [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.
• [38] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC. SSD: single shot multibox detector. European Conference on Computer Vision 2016:21–37.
• [39] Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition 2009:248–55.
• [40] Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A. The pascal visual object classes challenge: a retrospective. Int J Comput Vision 2015;111(1):98–136.
• [41] Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL, Microsoft COCO. Common objects in context. European Conference on Computer Vision 2014:740–55.
• [42] Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. IEEE Conference on Computer Vision and Pattern Recognition 2017:2881–90.
• [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).