Breast lesion segmentation and characterization using the Small Tumor-Aware Network (STAN) and 2D/3D shape descriptors in ultrasound images

Breast cancer is the second leading cause of cancer death in women worldwide. Ultrasound is one of the most used tools for image-based assessment of this disease, it helps to discriminate between benign or malignant masses, however, it depends largely on the radiologists experience. In recent years there has been a special interest to develop automatic segmentation systems (radiologist independent) for ultrasound images. The main challenges to get a clear ultrasound segmentation are: speckle noise, low contrast, blurred edges, shadows, etc. In this work, we use the Small Tumor Aware Network (STAN) architecture to automatically segment the images and we validate the network using eleven metrics on five combined breast ultrasound datasets. Dice’s coefficient indicates that predicted segmentations are 84% similar to the ground truth while high recall, specificity and accuracy results are being obtained. We use this information to calculate the tortuosity to differentiate between malignant and benign lesions, the Wilcoxon test results are p < 0.05 with a z = 2.73.