ArticleJournal of magnetic resonance imaging : JMRI2009
Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.
Article in Journal of magnetic resonance imaging : JMRI, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 22 citations in OpenAlex.
- Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.BMC medical imaging · 2024Article
- Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.Academic radiology · 2023Article
- Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.Frontiers in radiology · 2021Review
- Breast Contrast Enhanced MR Imaging: Semi-Automatic Detection of Vascular Map and Predominant Feeding Vessel.PloS one · 2016Article
- Age- and race-dependence of the fibroglandular breast density analyzed on 3D MRI.Medical physics · 2010Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo develop a computer-based algorithm for detecting blood vessels that appear in breast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and to evaluate the improvement in reducing the number of vascular pixels that are labeled by computer-aided diagnosis (CAD) systems as being suspicious of malignancy. MATERIALS AND
methodsThe analysis was performed in 34 cases. The algorithm applied a filter bank based on wavelet transform and the Hessian matrix to detect linear structures as blood vessels on a two-dimensional maximum intensity projection (MIP). The vessels running perpendicular to the MIP plane were then detected based on the connectivity of enhanced pixels above a threshold. The nonvessel enhancements were determined and excluded based on their morphological properties, including those showing scattered small segment enhancements or nodular or planar clusters. The detected vessels were first converted to a vasculature skeleton by thinning and subsequently compared to the vascular track manually drawn by a radiologist.
resultsWhen evaluating the performance of the algorithm in identifying vascular tissue, the correct-detection rate refers to pixels identified by both the algorithm and radiologist, while the incorrect-detection rate refers to pixels identified by only the algorithm, and the missed-detection rate refers to pixels identified only by the radiologist. From 34 analyzed cases the median correct-detection rate was 85.6% (mean 84.9% +/- 7.8%), the incorrect-detection rate was 13.1% (mean 15.1% +/- 7.8%), and the missed-detection rate was 19.2% (mean 21.3% +/- 12.8%). When detected vessels were excluded in the hot-spot color-coding of the CAD system, they could reduce the labeling of vascular vessels in 2.6%-68.6% of hot-spot pixels (mean 16.6% +/- 15.9%).
conclusionThe computer algorithm-based method can detect most large vessels and provide an effective means in reducing the labeling of vascular pixels as suspicious on a DCE-MRI CAD system. This algorithm may improve the workflow of radiologists using CAD for image display, but will be particularly useful for development of automated CAD that gives diagnostic impression.
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