计算机科学
概化理论
特征(语言学)
结肠镜检查
边界(拓扑)
模式识别(心理学)
相似性(几何)
图像分割
分割
市场细分
图像(数学)
人工智能
计算机视觉
结直肠癌
医学
癌症
数学
统计
内科学
数学分析
哲学
业务
语言学
营销
作者
Deng-Ping Fan,Ge-Peng Ji,Tao Zhou,Geng Chen,Huazhu Fu,Jianbing Shen,Ling Shao
标识
DOI:10.1007/978-3-030-59725-2_26
摘要
Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challenging task, for two major reasons: (i) the same type of polyps has a diversity of size, color and texture; and (ii) the boundary between a polyp and its surrounding mucosa is not sharp. To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. Specifically, we first aggregate the features in high-level layers using a parallel partial decoder (PPD). Based on the combined feature, we then generate a global map as the initial guidance area for the following components. In addition, we mine the boundary cues using the reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues. Thanks to the recurrent cooperation mechanism between areas and boundaries, our PraNet is capable of calibrating some misaligned predictions, improving the segmentation accuracy. Quantitative and qualitative evaluations on five challenging datasets across six metrics show that our PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency (\(\varvec{\sim }\)50 fps).
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