A novel automatic segmentation workflow of axial breast DCE-MRI

The present paper proposes a fully automated three-dimensional (3-D) system for breast and lesion segmentation of Dynamic Contrast Enhanced MRI (DCE-MRI). Such a system as the Computer-Aided Diagnostic system (CAD) can be used to support radiologists by marking suspicious areas. The proposed 3-D-CAD system includes three modules. The first one concerns breast area segmentation based on image content analysis, the Moore-Neighbor tracing algorithm, and the Dijkstra algorithm. The second one concerns the automation of locating and selecting lesions that starts by preprocessing the already segmented breast regions; then, a K-means algorithm allows extraction of regions including suspicious tissues. The third one is the superimposition of all detected lesions from selected slices to create a 3-D view of the lesion. The 3-D reconstruction is based on the Marching Cube algorithm. The validation of breast area segmentation reveals the robustness of the proposed process versus different breast densities, complex forms, and challenging cases. The segmentation of the breast part from 120 slices with the proposed method is achieved in 20.57 ± 5.2 s, which is faster than existing methods. In addition to calculated metrics as Dice similarity coefficient (DSC) and rates of true positives, the module of lesion extraction is validated by experimented radiologists. The proposed workflow of this module shows competitive results compared to the existing methods of automated lesion segmentation and a total ability for eliminating the extra regions to lesions.

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A novel automatic segmentation workflow of axial breast DCE-MRI

Cited by 2 publications

References 11 publications

A Novel CAD System for Breast DCE-MRI Based on Textural Analysis Using Several Machine Learning Methods

A Novel CAD System for Breast DCE-MRI Based on Textural Analysis Using Several Machine Learning Methods

A Novel 3-D-CAD for Breast and Lesion Segmentation of Axial Breast DCE-MRI

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