Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks
Source abstract
Abstract
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is regarded as one of the main diagnostic tools for breast cancer. Several methodologies have been developed to automatically localize suspected malignant breast lesions. Changes in tissue appearance in response to the injection of the contrast agent (CA) are indicative of the presence of malignant breast lesions. However, these changes are extremely similar to the ones of internal organs, such as the heart. Thus, the task of chest cavity segmentation is necessary for the development of lesion detection. In this work, a data-efficient approach is proposed, to automatically segment breast MRI data. Specifically, a study on several UNet-like architectures (Dynamic UNet) based on ResNet is presented. Experiments quantify the impact of several additions to baseline models of varying depth, such as self-attention and the presence of a bottlenecked connection. The proposed methodology is demonstrated to outperform the current state of the art both in terms of data efficiency and in terms of similarity index when compared to manually segmented data.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: MRI in cancer diagnosis · AI in cancer detection · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Brunel Innovation Centre, Brunel University London, Uxbridge UB8 3PH, UK
Healthcare Innovation Centre, Teesside University, Middlesbrough TS1 3BX, UK
School of Kinesiology, University of Michigan, Ann Arbor, MI 48109, USA
Electronic and Computer Engineering Department, Brunel University London, Uxbridge UB8 3PH, UK
Funding
UK Research and Innovation · Brunel University London
References (46)
Show source-provided references
1. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries10.3322/caac.21492 ↗
2. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries10.3322/caac.21660 ↗
3. Bray, Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 201210.1002/ijc.29210 ↗
4. How I report breast magnetic resonance imaging studies for breast cancer staging and screening10.1186/s40644-016-0078-0 ↗
5. Breast cancer: Early prediction of response to neoadjuvant chemotherapy using parametric response maps for MR imaging10.1148/radiol.14131332 ↗
6. Zbontar, J., Knoll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M.J., Defazio, A., Stern, R., Johnson, P., and Bruno, M. (2018). fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. arXiv.
7. Magnetic Resonance Spectroscopy and Imaging Guidance in Molecular Medicine: Targeting and Monitoring of Choline and Glucose Metabolism in Cancer10.1016/j.mri.2011.12.002 ↗
8. Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks10.1016/j.artmed.2019.101781 ↗
9. Marrone, S., Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., and Sansone, C. (2016, January 4–8). Breast segmentation using Fuzzy C-Means and anatomical priors in DCE-MRI. Proceedings of the 23rd IEEE International Conference on Pattern Recognition (ICPR), Cancun, Mexico.10.1109/ICPR.2016.7899845 ↗
10. Kayalibay, B., Jensen, G., and van der Smagt, P. (2017). CNN-based Segmentation of Medical Imaging Data. arXiv.
11. Automatic Segmentation in Breast Cancer Using Watershed Algorithm
12. Wang, L., Platel, B., Ivanovskaya, T., Harz, M., and Hahn, H.K. (2012, January 2–5). Fully automatic breast segmentation in 3D breast MRI. Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain.10.1109/ISBI.2012.6235732 ↗
13. Performance of a Fully Automatic Lesion Detection System for Breast DCE-MRI10.1002/jmri.22680 ↗
14. Automatic atlas-based segmentation of the breast in MRI for 3D breast volume computation10.1118/1.4748504 ↗
15. Breast segmentation and density estimation in breast MRI: A fully automatic framework10.1109/JBHI.2014.2311163 ↗
16. Automated Segmentation of Breast in 3D MR Images Using a Robust Atlas10.1109/TMI.2014.2347703 ↗
17. Automatic segmentation of whole breast using atlas approach and deformable image registration10.1016/j.ijrobp.2008.07.001 ↗
18. Fooladivanda, A., Shokouhi, S.B., Mosavi, M.R., and Ahmadinejad, N. (2014, January 26–28). Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model. Proceedings of the 21th IEEE Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran.10.1109/ICBME.2014.7043932 ↗
19. Mustra, M., and Bozek, J. (2009, January 18–23). Breast border extraction and pectoral muscle detection using wavelet decomposition. Proceedings of the IEEE EUROCON, St. Petersburg, Russia.10.1109/EURCON.2009.5167827 ↗
20. Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images
21. A review of the application of deep learning in medical image classification and segmentation10.21037/atm.2020.02.44 ↗
22. Using deep learning to segment breast and fibroglandular tissue in MRI volumes10.1002/mp.12079 ↗
23. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
24. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–8). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA.
25. Bernal, J., and Histace, A. (2021). Computer-Aided Analysis of Gastrointestinal Videos, Springer.10.1007/978-3-030-64340-9 ↗
26. Maitra, D.S., Bhattacharya, U., and Parui, S.K. (2015, January 23–26). CNN based common approach to handwritten character recognition of multiple scripts. Proceedings of the 13th IEEE International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia.10.1109/ICDAR.2015.7333916 ↗
27. Raghu, M., Zhang, C., Kleinberg, J., and Bengio, S. (2019, January 8–14). Transfusion: Understanding Transfer Learning for Medical Imaging. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada.
28. Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4–7). A Survey on Deep Transfer Learning. Proceedings of the Artificial Neural Networks and Machine Learning—ICANN 2018, Rhodes, Greece.10.1007/978-3-030-01424-7_27 ↗
29. Deep learning in medical imaging: A brief review10.1002/ett.4080 ↗
30. Zhang, H., Dauphin, Y.N., and Ma, T. (2019). Fixup Initialization: Residual Learning Without Normalization. arXiv.
31. He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7–13). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the International Conference on Computer Vision ICCV, Santiago, Chile.10.1109/ICCV.2015.123 ↗
32. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
33. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
34. Deep Learning for Medical Image Processing: Overview, Challenges and the Future
35. Bahdanau, D., Cho, K., and Bengio, Y. (2014, January 14–16). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 2nd International Conference on Learning Representations, Banff, AB, Canada.
36. Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., and He, X. (2017, January 21–26). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2018.00143 ↗
37. Yang, Z., He, X., Gao, J., Deng, L., and Smola, A. (2015). Stacked attention networks for image question answering. arXiv.10.1109/CVPR.2016.10 ↗
38. Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., and Wierstra, D. (2015). Draw: A recurrent neural network for image generation. arXiv.
39. Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., and Yang, Y. (2018). Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification. arXiv.
40. Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9–15). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA.
41. Deconvolution and Checkerboard Artifacts10.23915/distill.00003 ↗
42. Mikolajczyk, A., and Grochowski, M. (2018, January 9–12). Data augmentation for improving deep learning in image classification problem. Proceedings of the IEEE International Interdisciplinary PhD Workshop (IIPhDW), Świnouście, Poland.10.1109/IIPHDW.2018.8388338 ↗
43. Wong, S.C., Gatt, A., Stamatescu, V., and McDonnell, M.D. (December, January 30). Understanding data augmentation for classification: When to warp?. Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia.
44. Kingma, D.P., and Ba, J. (2014). A method for stochastic optimization. arXiv.
45. Smith, L.N. (2018). A disciplined approach to neural network hyper-parameters: Part 1—Learning rate, batch size, momentum, and weight decay. arXiv.
46. Smith, L.N. (2015). No more pesky learning rate guessing games. arXiv.