This project seeks to propose and implement a fully automated algorithm pipeline for identification, segmentation and quantification of vascular structure in breast cancer using clinical DCE-MR images. Experiments were conducted and the results obtained were analyzed comparatively to build association with the patients pathological responses.
The project was conducted in 4 stages.
- Preprocessing: MIP, contrast enhancement
- Segmentation: Frangi's filtering
- Skeletonization: global thresholding, connected components analysis, morphological reconstruction
- Quantification: vessel length and radius
Thanks to professor Christine Hendon for the lectures and advice.
Many loves and thanks to Lucy, who has been selflessly providing professional interpretations and insights from the perspective of a clinical medical student dedicated in the diagnosis and treatment of breast cancer.
[1] G. Carpentier et al., “Angiogenesis Analyzer for ImageJ — A comparative morphometric analysis of ‘Endothelial Tube Formation Assay’ and ‘Fibrin Bead Assay,’” Sci Rep, vol. 10, no. 1, Art. no. 1, Jul. 2020, doi: 10.1038/s41598-020-67289-8.
[2] C. N. Doukas, I. Maglogiannis, A. Chatziioannou, and A. Papapetropoulos, “Automated angiogenesis quantification through advanced image processing techniques,” Conf Proc IEEE Eng Med Biol Soc, vol. 2006, pp. 2345–2348, 2006, doi: 10.1109/IEMBS.2006.260675.
[3] M. Wang, L.-L. S. Ong, J. Dauwels, and H. H. Asada, “Automated tracking and quantification of angiogenic vessel formation in 3D microfluidic devices,” PLoS ONE, vol. 12, no. 11, p. e0186465, Nov. 2017, doi: 10.1371/journal.pone.0186465.
[4] A. Petrillo et al., “Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel,” PLOS ONE, vol. 11, no. 8, p. e0161691, Aug. 2016, doi: 10.1371/journal.pone.0161691.
[5] N. Onishi et al., “Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response,” Radiology, vol. 301, no. 2, pp. 295–308, Nov. 2021, doi: 10.1148/radiol.2021203645.
[6] C. N. Doukas, I. Maglogiannis, and A. A. Chatziioannou, “Computer-Supported Angiogenesis Quantification Using Image Analysis and Statistical Averaging,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 5, pp. 650–657, Sep. 2008, doi: 10.1109/TITB.2008.926463.
[7] T. Nielsen, T. Wittenborn, and M. R. Horsman, “Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Preclinical Studies of Antivascular Treatments,” Pharmaceutics, vol. 4, no. 4, pp. 563–589, Nov. 2012, doi: 10.3390/pharmaceutics4040563.
[8] L.-A. Wu et al., “Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient,” J Magn Reson Imaging, vol. 42, no. 5, pp. 1407–1420, Nov. 2015, doi: 10.1002/jmri.24915.
[9] Y. Zhong, “Extracting Vessel Structure From 3D Image Data,” p. 98.
[10] A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” in Medical Image Computing and Computer-Assisted Intervention — MICCAI’98, vol. 1496, W. M. Wells, A. Colchester, and S. Delp, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 130–137. doi: 10.1007/BFb0056195.
[11] M. E. Seaman, S. M. Peirce, and K. Kelly, “Rapid Analysis of Vessel Elements (RAVE): A Tool for Studying Physiologic, Pathologic and Tumor Angiogenesis,” PLoS One, vol. 6, no. 6, p. e20807, Jun. 2011, doi: 10.1371/journal.pone.0020807.
[12] B. J. Vakoc et al., “Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging,” Nat Med, vol. 15, no. 10, pp. 1219–1223, Oct. 2009, doi: 10.1038/nm.1971.
[13] W. Huang et al., “Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge,” Translational Oncology, vol. 7, no. 1, pp. 153–166, Feb. 2014, doi: 10.1593/tlo.13838.