Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
This work addresses the problem of improving breast cancer diagnosis for medical professionals by providing a semi-automatic segmentation tool, though it appears incremental as it builds on existing watershed methods with expert input.
The paper tackles the challenge of accurately segmenting breast lesions in MRI, which is difficult due to their complex shapes and intensity variations, by proposing a marker-controlled watershed transformation method that uses expert-determined markers; it achieved an average Dice coefficient of 0.7808 and Jaccard index of 0.6704 on 106 lesions.
Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step in evaluating tumor volume and in the quantification of tumor characteristics. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and high variance in their intensity distribution across patients. In this paper, we propose a novel marker-controlled watershed transformation-based approach, which uses the brightest pixels in a region of interest (determined by experts) as markers to overcome this challenge, and accurately segment lesions in breast MRI. The proposed approach was evaluated on 106 lesions, which includes 64 malignant and 42 benign cases. Segmentation results were quantified by comparison with ground truth labels, using the Dice similarity coefficient (DSC) and Jaccard index (JI) metrics. The proposed method achieved an average Dice coefficient of 0.7808$\pm$0.1729 and Jaccard index of 0.6704$\pm$0.2167. These results illustrate that the proposed method shows promise for future work related to the segmentation and classification of benign and malignant breast lesions.