PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network

Rapid and precise segmentation of breast tumors is a severe challenge for the global research community to diagnose breast cancer in younger females. An ultrasound system is a non-invasive and efficient way of breast screening. The area, shape, and texture of different breast tumors play a vital role for clinicians in making accurate diagnostic decisions. Furthermore, the limited availability of breast tumor annotated datasets is another challenge for properly training deep neural networks. This research proposes a semi-supervised learning-based method, which incorporates a Data expansion network (DEN), Probability map generator network (PMG), and U-shaped pyramid-dilated fusion network (PDF-UNet) for accurate breast tumor segmentation. The first DEN network is trained on breast unannotated tumor images and generates synthetic images for the data expansion task. The second PMG network generates corresponding probability map images against synthetic unannotated images. Finally, we proposed a segmentation network (PDF-UNet), a modified variant of UNet, to segment the breast tumor images. The results demonstrate that compared with classical UNet, our proposed PDF-UNet achieves an increment of DSC (2.42%) on the Mendeley dataset and an increment of DSC (1.52%) observed on the SIIT dataset. The results reflect that the proposed method is effective when annotated breast ultrasound data is insufficient to train the network. Furthermore, the proposed method can be helpful in relieving the annotation burden of radiologists. The implementation source code is available at GitHub: https://github.com/ahmedeqbal/PDF-UNet.