B-Mode Ultrasound Breast Anatomy Segmentation
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Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed.
Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with \(K=6\). The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging.
KeywordsSegmentation Rigid registration Minimum path B-mode Ultrasound Anatomical structures Breast cancer
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