AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses

Abstract

Diverse breast masses in size, shape and place make accurate image segmentation more challenging in a unified deep-learning network. Therefore, based on the U-net network, an adaptive automatic whole-field segmentation network (AWDS-net) for characterising diverse breast masses is proposed to assist more accurate and fast medical diagnosis in this paper. In the encoder part of AWDS-net, a small mass extraction mechanism (SMEM) is designed to better retain fine-grained small mass location information, while a spatial pyramid module (SPM) is added to capture multi-scale context and high-resolution image information. In the decoder part, an attention gate (AG) mechanism is inserted to make the model automatically focus on the useful target region information, so that the extracted feature information can be used to build a symmetric encoder-decoder structure for automatic segmentation network of multiple masses in the full field of view. The experimental results on an open-source breast cancer dataset digital database for mammography (DDSM) show that compared with U-net, Attention-Unet, R2U-Net, and SegNet, the proposed AWDS-net achieves, up to higher image segmentation metrics of 3.16% accuracy, 20.59% sensitivity, 5.23% specificity,10.27% precision, 15.08% IoU and 14.21% F1-score with acceptable training time.

KEYWORDS:

• Mammography
• mass segmentation
• whole-field vision
• U-net

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by Shanghai Pujiang Program numbered 21PJD026.

Notes on contributors

Jiajia Jiao

Jiajia Jiao is an associate professor at Shanghai Maritime University and her research interests include machine learning-assisted medical image analysis and computer optimisation.

Yingzhao Chen

Yingzhao Chen was an M.S. student at Shanghai Maritime University and her research interests include machine learning-assisted medical image analysis.

Zhiyu Li

Zhiyu Li is a doctor at Shanghai East Hospital and Tongji University School of Medicine. Her research interest focuses on medical image processing and analysis.

Tien-Hsiung Weng

Tien-Hsiung Weng is with Providence University and his research interests include machine learning-assisted applications.