Blood Vessel Segmentation in Breast MRI: Comprehensive Review of Techniques and Challenges

Angiogenesis is the ongoing formation of new blood vessels from existing ones, occurring throughout life in both healthy and diseased states.  Furthermore, it plays a crucial role in the development and progression of breast cancer.  Magnetic Resonance Imaging (MRI) is a sensitive, non-invasive technique for monitoring and identifying lesions, establishing it as standard clinical practice.  However, its effectiveness in visualizing blood vessels in breast tissue requires further investigation.  Blood vessel analysis provides valuable insights into tumor progression and information that can be correlated with the underlying tumor biology.  This paper presents a comprehensive review of techniques and methodologies.  A key contribution of this work is the proposal of a consolidated workflow that synthesizes the strengths of the various approaches reviewed, offering a more integrated solution to blood vessel segmentation in breast MRI.  The paper also examines the challenges and limitations in this field, including image quality, algorithmic constraints, anatomical complexities, and data scarcity.  Our study identifies ongoing issues, particularly the need for robust evaluation metrics and standardized datasets.  Addressing these issues is essential for driving future advancements in breast MRI vessel segmentation and improving clinical outcomes.

  1. Cuthrell K. M., Tzenios N.  Breast Cancer: Updated and Deep Insights.  6, 104–118 (2023).
  2. Ginsburg O., Yip C.-H., Brooks A., et al.  Breast cancer early detection: A phased approach to implementation.  Cancer.  126, 2379–2393 (2020).
  3. Obeagu E. I., Obeagu G. U.  Breast cancer: A review of risk factors and diagnosis.  Medicine.  103 (3), e36905 (2024).
  4. Lee C. S., Moy L., Joe B. N., Sickles E. A., Niell B. L.  Screening for Breast Cancer in Women Age 75 Years and Older.  American Journal of Roentgenology.  210 (2), 256–263 (2018).
  5. Wright K., Sridevan S., Neupane A., Panjiyar B.  Targeting Angiogenesis in Cancer Therapy.  Available at SSRN: https://ssrn.com/abstract=4648070 (2023).
  6. Adair T. H., Montani J.-P.  Overview of Angiogenesis.  Angiogenesis. Morgan & Claypool Life Sciences (2010).
  7. Madu C. O., Wang S., Madu C. O., Lu Y.  Angiogenesis in Breast Cancer Progression, Diagnosis, and Treatment.  Journal of Cancer.  11 (15), 4474–4494 (2020).
  8. Xiao J., Rahbar H., Hippe D. S., et al.  Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis.  npj Breast Cancer.  7, 42 (2021).
  9. Iranmakani S., Mortezazadeh T., Sajadian F., et al.  A review of various modalities in breast imaging: technical aspects and clinical outcomes.  Egyptian Journal of Radiology and Nuclear Medicine.  51, 57 (2020).
  10. Bhushan A., Gonsalves A., Menon J. U.  Current State of Breast Cancer Diagnosis, Treatment, and Theranostics.  Pharmaceutics.  13 (5), 723 (2021).
  11. Moccia S., De Momi E., El~Hadji S., Mattos L. S.  Blood vessel segmentation algorithms – Review of methods, datasets and evaluation metrics.  Computer Methods and Programs in Biomedicine.  158, 71–91 (2018).
  12. Yepes-Calderon F., McComb J. G.  Manual Segmentation Errors in Medical Imaging. Proposing a Reliable Gold Standard.  Applied Informatics.  230–241 (2019).
  13. Liu Q., Liu Z., Yong S., Jia K., Razmjooy N.  Computer-aided breast cancer diagnosis based on image segmentation and interval analysis.  Automatika.  61 (3), 496–506 (2020).
  14. Bruno F., Granata V., Cobianchi Bellisari F., et al.  Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine.  Cancers.  14 (7), 1626 (2022).
  15. Gabbert D. D., Kheradvar A., Jerosch-Herold M., Oechtering T. H., Uebing A. S., Kramer H.-H., Voges I., Rickers C.  MRI-based comprehensive analysis of vascular anatomy and hemodynamics.  Cardiovascular Diagnosis & Therapy.  11 (6), 1367–1378 (2021).
  16. Samuel P. M., Veeramalai T.  Review on retinal blood vessel segmentation – an algorithmic perspective.  International Journal of Biomedical Engineering and Technology.  34 (1), 75–105 (2020).
  17. Verma P. K., Kaur J.  Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.  Journal of Digital Imaging in Medical Informatics.  37, 1783–1799 (2024).
  18. Ciecholewski M., Kassjański M.  Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review.  Sensors.  21 (6), 2027 (2021).
  19. Goni M. R., Ruhaiyem N. I. R., Mustapha M., Achuthan A., Che Mohd Nassir C. M. N.  Brain Vessel Segmentation Using Deep Learning – A Review.  IEEE Access.  10, 111322–111336 (2022).
  20. Van Beek E. J. R., Kuhl C., Anzai Y., et al.  Value of MRI in medicine: More than just another test?  Journal of Magnetic Resonance Imaging.  49 (7), e14–e25 (2019).
  21. Bullmore E.  The future of functional MRI in clinical medicine.  NeuroImage.  62 (2), 1267–1271 (2012).
  22. Galati F., Rizzo V., Trimboli R. M., Kripa E., Maroncelli R., Pediconi F.  MRI as a biomarker for breast cancer diagnosis and prognosis.  BJR Open.  4 (1), 20220002 (2022).
  23. Goncalves M. A., Pereira B. T. L., Tavares C. A., Santos T. M. R., da Cunha E. F. F., Ramalho T. C.  Value of Contrast-enhanced Magnetic Resonance Imaging (MRI) in the Diagnosis of Breast Cancer.  Mini Reviews in Medicinal Chemistry.  22 (6), 865–872 (2022).
  24. Gierlinger M., Brandner D. M., Zagar B. G.  Segmentation of elongated structures processed on breast MRI for the detection of vessels.  tm – Technisches Messen.  88 (7–8), 481–487 (2021).
  25. Glotsos D., Vassiou K., Kostopoulos S., et al.  A modified Seeded Region Growing algorithm for vessel segmentation in breast MRI images for investigating the nature of potential lesions.  Journal of Physics: Conference Series.  490, 012136 (2014).
  26. Kahala G., Sklair M., Spitzer H.  Multi-Scale Blood Vessel Detection and Segmentation in Breast MRIs.  Journal of Medical and Biological Engineering.  39, 424–430 (2019).
  27. Vignati A., Giannini V., Bert A., et al.  A Fully Automatic Multiscale 3-Dimensional Hessian-Based Algorithm for Vessel Detection in Breast DCE-MRI.  Investigative Radiology.  47 (12), 705–710 (2012).
  28. Lin M., Chen J.-H., Nie K., Chang D., Nalcioglu O., Su M.-Y.  Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.  Journal of Magnetic Resonance Imaging.  30 (4), 817–824 (2009).
  29. Zaman T., Hossain Q. D.  An Efficient Technique for Detection and Intensification of Blood Vessels from Breast MRI.  2021 5th International Conference on Electrical Information and Communication Technology (EICT).  1–7 (2021).
  30. Lew C., Harouni M., Kirksey E., et al.  A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.  Scientific Reports. 14, 5383 (2024).
  31. Guan X., Su J., Pan H., Zhang Z., Gong H.  An Image Enhancement Method Based on Gamma Correction.  2009 Second International Symposium on Computational Intelligence and Design.  60–63 (2009).
  32. Zuiderveld K. Contrast limited adaptive histogram equalization.  Graphics Gems IV.  474–485 (1994).
  33. Adelmann H. G.  Butterworth equations for homomorphic filtering of images.  Computers in Biology and Medicine.  28 (2), 169–181 (1998).
  34. Villar S. A., Torcida S., Acosta G. G.  Median Filtering: A New Insight.  Journal of Mathematical Imaging and Vision.  58, 130–146 (2017).
  35. D'Haeyer J. P. F.  Gaussian filtering of images: A regularization approach.  Signal Processing.  18 (2), 169–181 (1989).
  36. Tomasi C., Manduchi R.  Bilateral filtering for gray and color images.  Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).  839–846 (1998).
  37. Celik T.  Two-dimensional histogram equalization and contrast enhancement.  Pattern Recognition.  45 (10), 3810–3824 (2012).
  38. Mehrotra R., Namuduri K. R., Ranganathan N.  Gabor filter-based edge detection.  Pattern Recognition.  25 (12), 1479–1494 (1992).
  39. Shi Z., Chen Y., Gavves E., Mettes P., Snoek C. G. M.  Unsharp Mask Guided Filtering.  IEEE Transactions on Image Processing.  30, 7472–7485 (2021).
  40. Xiurong T.  The application of adaptive unsharp mask algorithm in medical image enhancement.  Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference.  1368–1370 (2011).
  41. Braik M., Sheta A., Ayesh A.  Particle swarm optimisation enhancement approach for improving image quality.  International Journal of Innovative Computing and Applications.  1 (2), 138–145 (2007).
  42. Agam G., Dinstein I.  Regulated morphological operations.  Pattern Recognition.  32 (6), 947–971 (1999).
  43. Ackora-Prah J., Ayekple Y., Acquah R., Andam P., Sakyi E., Gyamfi D.  Revised Mathematical Morphological Concepts.  Advances in Pure Mathematics.  5 (4), 155–161 (2015).
  44. Al Najjar M., Ghantous M., Bayoumi M.  Hysteresis Thresholding.  Video Surveillance for Sensor Platforms: Algorithms and Architectures.  147–174 (2014).
  45. Mehnert A., Jackway P.  An improved seeded region growing algorithm.  Pattern Recognition Letters.  18 (10), 1065–1071 (1997).
  46. Naji M. A., El Filali S., Aarika K., Benlahmar E. H., Ait Abdelouhahid R., Debauche O.  Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis.  Procedia Computer Science.  191, 487–492 (2021).
  47. El Jiani L., El Filali S., Benlahmar E. H.  Overcome medical image data scarcity by data augmentation techniques: A review.  2022 International Conference on Microelectronics (ICM).  21–24 (2022).
  48. El Jiani L., El Filali S., Ben Lahmar E. H., Haloum I.  Deep Learning-Based Approaches Using Medical Imaging for Therapy Response Prediction in Breast Cancer: A Systematic Literature Review.  International Journal of Online and Biomedical Engineering (iJOE).  20 (12), 37–54 (2024).
  49. Müller D., Soto-Rey I., Kramer F.  Towards a guideline for evaluation metrics in medical image segmentation.  BMC Research Notes.  15, 210 (2022).
  50. Fidvi S., Holder J., Li H., Parnes G. J., Shamir S. B., Wake N.  Advanced 3D Visualization and 3D Printing in Radiology.  Biomedical Visualisation.  103–138 (2023).