ASU-Net: U-shape adaptive scale network for mass segmentation in mammograms

U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively.

[1]  Stuart Crozier,et al.  Fully Automatic Computer-aided Mass Detection and Segmentation via Pseudo-color Mammograms and Mask R-CNN , 2019, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[2]  Berkman Sahiner,et al.  Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization , 2001, IEEE Transactions on Medical Imaging.

[3]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  M. Rawashdeh,et al.  Staying abreast of imaging - Current status of breast cancer detection in high density breast. , 2020, Radiography.

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Gustavo Carneiro,et al.  Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[7]  Gustavo Carneiro,et al.  Deep structured learning for mass segmentation from mammograms , 2014, 2015 IEEE International Conference on Image Processing (ICIP).

[8]  Sara Tedmori,et al.  Mammogram image visual enhancement, mass segmentation and classification , 2015, Appl. Soft Comput..

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Xiaoying Wang,et al.  Fully automatic segmentation on prostate MR images based on cascaded fully convolution network , 2018, Journal of magnetic resonance imaging : JMRI.

[11]  Nima Tajbakhsh,et al.  UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation , 2020, IEEE Transactions on Medical Imaging.

[12]  Jaime S. Cardoso,et al.  Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer , 2015, Int. J. Pattern Recognit. Artif. Intell..

[13]  D. A. May-Arrioja,et al.  Digital Image Processing Technique for Breast Cancer Detection , 2013 .

[14]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[15]  M. Gemignani.  Breast cancer screening: why, when, and how many? , 2011, Clinical obstetrics and gynecology.

[16]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[17]  Rangaraj M. Rangayyan,et al.  Polygonal Modeling of Contours of Breast Tumors With the Preservation of Spicules , 2008, IEEE Transactions on Biomedical Engineering.

[18]  Dongdong Chen,et al.  A Deep Dual-path Network for Improved Mammogram Image Processing , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[19]  Yuchen Zhang,et al.  A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks , 2020 .

[20]  Alexander Horsch,et al.  Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies , 2011, International Journal of Computer Assisted Radiology and Surgery.

[21]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[23]  A. McTiernan,et al.  World Cancer Research Fund International: Continuous Update Project—systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk , 2019, Cancer Causes & Control.

[24]  Anne L. Martel,et al.  Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening , 2014, Journal of Digital Imaging.

[25]  C. D'Orsi,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. , 2010, Journal of the American College of Radiology : JACR.

[26]  Alan C. Bovik,et al.  Computer-Aided Detection of Breast Cancer – Have All Bases Been Covered? , 2008, Breast cancer : basic and clinical research.

[27]  I. Vejborg,et al.  Breast cancer survivors' risk of interval cancers and false positive results in organized mammography screening , 2020, Cancer medicine.

[28]  Jian Yang,et al.  Selective Kernel Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[30]  Tae-Seong Kim,et al.  A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification , 2018, Int. J. Medical Informatics.

[31]  Jaime S. Cardoso,et al.  INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.

[32]  Lars J. Grimm,et al.  Breast Cancer Radiogenomics: Current Status and Future Directions. , 2020, Academic radiology.

[33]  Ahmed M. Kabel,et al.  Breast Cancer: Insights into Risk Factors, Pathogenesis, Diagnosis and Management , 2015 .

[34]  Mike E. Davies,et al.  Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net , 2018, RAMBO+BIA+TIA@MICCAI.

[35]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[37]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  László Tabár,et al.  The incidence of fatal breast cancer measures the increased effectiveness of therapy in women participating in mammography screening , 2018, Cancer.

[39]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[40]  D. Kallmes,et al.  A second look at the second-look angiogram in cases of subarachnoid hemorrhage. , 1997, Radiology.

[41]  Gang Yu,et al.  Learning a Discriminative Feature Network for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[42]  Robert T. Fazzio,et al.  Breast Cancer Screening for Women at Average Risk , 2019, Current Breast Cancer Reports.

[43]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Gregory D. Hager,et al.  Adversarial deep structured nets for mass segmentation from mammograms , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).