A new background distribution‐based active contour model for three‐dimensional lesion segmentation in breast DCE‐MRI. Issue 8 (30th November 2016) - Liu, Hui; Liu, Yiping; Zhao, Zuowei; Zhang, Lina; Qiu, Tianshuang
Abstract : Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three‐dimensional lesions from dynamic contrast‐enhanced magnetic resonance images (DCE‐MRIs) of the breast. Methods: The authors propose a new background distribution‐based active contour model using level set (BDACMLS) to segment lesions in breast DCE‐MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors' method. Finally, the performance of the proposed method is evaluated by several region‐based metrics such as the overlap ratio. Results: Forty‐two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinoma in situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE‐MRI three‐dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak‐edge‐passed problem, and improve the robustness in segmenting different lesions.