DCE-MRI with Function


== Abstract ==

<p><span style="font-size: 10.24px;">ABSTRACT: Accurate lesion segmentation is a critical component of computer-aided diagnosis for breast cancer, as it enables precise lesion delineation and robust quantitative assessment. However, breast lesion&nbsp;segmentation remains challenging because of tumor heterogeneity, variations in fibroglandular tissue,&nbsp;and the complex morphology of breast lesions. Dynamic contrast-enhanced magnetic resonance imaging&nbsp;(DCE-MRI) has emerged as an effective modality for the early detection and characterization of breast lesions 6 due to its ability to capture detailed information on tumor morphology and microenvironment. This study&nbsp;proposes a two-stage framework for breast DCE-MRI lesion segmentation. In the first stage, bounded turning&nbsp;Mittag-Leffler enhancement is applied to improve image quality and enhance lesion-relevant structures. In&nbsp;the second stage, an extended Visual Geometry Group (VGG)-based network with dilated convolution is employed to segment breast lesions. The proposed framework was evaluated on a public multicenter dataset comprising 979 pre-treatment T1-weighted cases and 10,863 bitmap slice images converted from original Neuroimaging Informatics Technology Initiative (NIFTI) volumes with expert voxel-level annotations. The&nbsp;dataset was divided into training, validation, and testing subsets at ratios of 68%, 12%, and 20%, respectively. Based on 5-fold cross-validation, the proposed method achieved an accuracy of 98.73%, a Dice coefficient of&nbsp;91.56%, and a Jaccard index of 85.41%. Furthermore, the framework demonstrated competitive performance compared with related studies. These findings confirm the effectiveness of the proposed framework for breast DCE-MRI lesion segmentation and highlight its potential to improve the accuracy and reliability of breast cancer computer-aided diagnosis systems.</span></p>



== Document ==
<pdf>Media:Draft_Shamkhi_552338848-7979-document.pdf</pdf>