Ultrasound Image Segmentation and its Evaluation using Various Encoder Architectures

Ultrasound imaging faces particular challenges with high inter-operator variability and manual inspection of abnormalities. Deep learning segmentation methods are progressing rapidly to address these clinical challenges with improved automatic segmentation performance combining convolutional neural network (CNN) and Transformer approaches. However, challenges still remain with poor performance in boundary areas due to high speckle noise and training models with limited training data. This paper demonstrates a comparison of EfficientNet B2, EfficientNet B7 and PVT-v2-B5 pre-trained encoder backbones in a U-Net architecture. A noticeable improvement for all three pre-trained backbones is shown particularly in smaller Breast Ultrasound datasets with limited training data (EfficientNet B2: 4.5%, EfficientNet B7: 3.9%, PVT-v2-B5: 2.4%). However, the improvement is marginal (less than 1%) in the larger Nerve Ultrasound dataset. In addition, we noticed that the performance across all backbones is better in segmenting regular regions of interest (e.g. benign breast lesions), over irregular shapes (e.g. malignant breast lesions). The code used for this study is available at: https://github.com/aimsgroup-Leeds/IEEECBMS2024_US_Seg