Spatial Attention Lesion Detection on Automated Breast Ultrasound

Wang, Feiqian; Liu, Xiaotong; Qian, Buyue; Ruan, Litao; Zhao, R.; Yin, Changchang; Yuan, Na; Wei, Rong; Ma, Xin; Wei, Jishang

doi:10.1007/978-3-030-17938-0_20

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Although various algorithms for breast ultrasound image segmentation continue to be proposed, it is still a difficult task. Breast lesions are usually hypoechoic in ultrasound images with various artefacts, blurred boundaries, low signal-tonoise ratios and uneven intensities, which makes segmentation difficult to automatically perform by computers [1]. Image segmentation methods based on deep learning are applied to various image segmentation tasks [12].…”

Section: Segmentation Approach For Breast Ultrasound Imagementioning

confidence: 99%

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Segmentation and recognition of breast ultrasound images based on an expanded U-Net

et al. 2021

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This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application scenarios of semantic segmentation of breast ultrasound images, and adds dropout layers to the U-Net architecture to reduce the redundancy in texture details and prevent overfitting. The main innovation of this paper is proposing an expanded training approach to obtain an expanded of U-Net. The output map of the expanded U-Net can retain texture details and edge features of breast tumours. Using the grey-level probability labels to train the U-Net is faster than using ordinary labels. The average Dice coefficient (standard deviation) and the average IOU coefficient (standard deviation) are 90.5% (±0.02) and 82.7% (±0.02), respectively, when using the expanded training approach. The Dice coefficient of the expanded U-Net is 7.6 larger than that of a general U-Net, and the IOU coefficient of the expanded U-Net is 11 larger than that of the general U-Net. The context of breast ultrasound images can be extracted, and texture details and edge features of tumours can be retained by the expanded U-Net. Using an expanded U-Net can quickly and automatically achieve precise segmentation and multi-class recognition of breast ultrasound images.

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Section: Segmentation Approach For Breast Ultrasound Imagementioning

confidence: 99%

“…Breast cancer is the most common malignancy in women and the main cause of cancer deaths among women worldwide [1]. It accounts for 30% of cancer diagnoses in women, with increasing incidence in recent years [2].…”

Section: Introductionmentioning

confidence: 99%