As one of the most common malignant tumors with high mortality rates among women worldwide, breast cancer relies highly on medical imaging for its diagnosis and treatment. Focus segmentation plays a crucial role in the precise identification of pathological regions, diagnostic assistance, and treatment planning. Recent advances in deep learning have made significant progress in automatic segmentation for breast cancer focus, laying a foundation for further deep learning-based research in this field. Recent research achievements are systematically reviewed, with a focus on the application of deep learning technology in focus segmentation under different medical imaging modes, aiming to provide a reference for the advancement of research in breast cancer focus segmentation. The relevant datasets and common evaluation metrics for image segmentation are briefly introduced. The image segmentation method for breast cancer based on deep learning is then systematically reviewed. The application of algorithms applied in different imaging modes is generalized. At last, current challenges for this technology are summarized. Future directions are also discussed based on limitations in the existing research.