Global guidance network for breast lesion segmentation in ultrasound images
نویسندگان
چکیده
Automatic breast lesion segmentation in ultrasound helps to diagnose cancer, which is one of the dreadful diseases that affect women globally. Segmenting regions accurately from image a challenging task due inherent speckle artifacts, blurry boundaries, and inhomogeneous intensity distributions inside regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results medical tasks. However, operations CNN often focus on local regions, suffer limited capabilities capturing long-range dependencies input image, resulting degraded accuracy. In this paper, we develop deep network equipped with global guidance block (GGB) boundary detection (BD) modules for boosting segmentation. The GGB utilizes multi-layer integrated feature map as information learn non-local both spatial channel domains. BD additional enhance quality result refinement. Experimental public dataset collected show our outperforms other methods recent semantic Moreover, also application prostate segmentation, method better identifies than state-of-the-art networks.
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ژورنال
عنوان ژورنال: Medical Image Analysis
سال: 2021
ISSN: ['1361-8423', '1361-8431', '1361-8415']
DOI: https://doi.org/10.1016/j.media.2021.101989