Accurate segmentation of breast tumor in ultrasound images through joint training and refined segmentation

Objective. This paper proposes an automatic breast tumor segmentation method for two-dimensional (2D) ultrasound images, which is significantly more accurate, robust, and adaptable than common deep learning models on small datasets. Approach. A generalized joint training and refined segmentation framework (JR) was established, involving a joint training module ( J module ) and a refined segmentation module ( R module ). In J module , two segmentation networks are trained simultaneously, under the guidance of the proposed Jocor for Segmentation (JFS) algorithm. In R module , the output of J module is refined by the proposed area first (AF) algorithm, and marked watershed (MW) algorithm. The AF mainly reduces false positives, which arise easily from the inherent features of breast ultrasound images, in the light of the area, distance, average radical derivative (ARD) and radical gradient index (RGI) of candidate contours. Meanwhile, the MW avoids over-segmentation, and refines segmentation results. To verify its performance, the JR framework was evaluated on three breast ultrasound image datasets. Image dataset A contains 1036 images from local hospitals. Image datasets B and C are two public datasets, containing 562 images and 163 images, respectively. The evaluation was followed by related ablation experiments. Main results. The JR outperformed the other state-of-the-art (SOTA) methods on the three image datasets, especially on image dataset B. Compared with the SOTA methods, the JR improved true positive ratio (TPR) and Jaccard index (JI) by 1.5% and 3.2%, respectively, and reduces (false positive ratio) FPR by 3.7% on image dataset B. The results of the ablation experiments show that each component of the JR matters, and contributes to the segmentation accuracy, particularly in the reduction of false positives. Significance. This study successfully combines traditional segmentation methods with deep learning models. The proposed method can segment small-scale breast ultrasound image datasets efficiently and effectively, with excellent generalization performance.