Segmentation of breast lesion using fuzzy thresholding and deep learning

Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has shown promise as a technique, providing enhanced temporal patterns of breast tissues. This study proposes an enhanced segmentation method for identifying breast lesions. The proposed experiments are done using the breast DCE-MRI images of 123 slices from seven patients in The Cancer Image Archive database. The three methods proposed and tested to segment the lesions are: i) Fuzzy C-mean Thresholding (FCMTH) with morphological operations ii) deep learning networks trained with original images iii) deep learning networks trained with three types of preprocessed images: the core breast image, the filtered core breast image, and the fuzzy thresholded image. In this study, the deep learning networks are trained with preprocessed images generated from the FCMTH technique, resulting in higher segmentation accuracy. FCMTH achieves Dice and Jaccard coefficients of 0.8458 and 0.7471, while DeepLabv3+ with MobileNetv2 trained by preprocessed images achieves 0.9468 and 0.8990, respectively. Thus, the combination of deep learning and FCMTH techniques provides the best performance for lesion detection.