Abstract
Early detection and diagnosis of breast cancer using ultrasound images are crucial for timely diagnostic decision and treatment in clinical application. However, the similarity between tumors and background and also severe shadow noises in ultrasound images make accurate segmentation of breast tumor challenging. In this paper, we propose a large pre-trained model for breast tumor segmentation, with robust performance when applied to new datasets. Specifically, our model is built upon UNet backbone with deep supervision for each stage of the decoder. Besides using Dice score, we also design discriminator-based loss on each stage of the decoder to penalize the distribution dissimilarity from multi-scales. Our proposed model is validated on a large clinical dataset with more than 10000 cases, and shows significant improvement than other representative models. Besides, we apply our large pretrained model to two public datasets without fine tuning, and obtain extremely good results. This indicates great generalizability of our large pre-trained model, as well as robustness to multi-site data. The code is publicly available at https://github.com/limy-ulab/US-SEG.
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Acknowledgment
This work was supported in part by The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006), National Natural Science Foundation of China (grant number 62131015), and Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600).
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Li, M. et al. (2023). Developing Large Pre-trained Model for Breast Tumor Segmentation from Ultrasound Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_9
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DOI: https://doi.org/10.1007/978-3-031-43990-2_9
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