A deep supervised transformer U‐shaped full‐resolution residual network for the segmentation of breast ultrasound image

Purpose Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors’ workload. In this article, we proposed a deep supervised transformer U‐shaped full‐resolution residual network (DSTransUFRRN) to segment BUS images. Methods In the proposed method, a full‐resolution residual stream and a deep supervision mechanism were introduced into TransU‐Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples. Results Cross‐validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04 ± 0.86%) than all compared methods (p