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基于改进mask r-cnn的细胞核图像分割方法
首发时间：2023-11-27
摘要：深度学习在医学领域的兴起推动了细胞核分割自动化的发展，但由于细胞核的聚集、粘附、形状差异和低对比度等问题，目前仍面临着难以捕捉有效特征和边缘模糊等困难。为解决上述问题，本文提出了一种注意力基于机制改进的mask r-cnn细胞核图像分割方法（ca-mask r-cnn），采用分离式的resnet增加网络基数的维度，提高网络语义提取能力；引入坐标注意力机制，重视空间信息，提升网络在细胞核图像识别上的性能；通过改进的软化非极大抑制筛选，提高对提高对团簇、重叠的细胞核检测效果；选择更为适当的损失函数，改善细胞核分割的效果。实验结果表明，ca-mask r-cnn的f1分数指标（f1-score）和叠加雅卡尔指数（aji）分别为0.8454和0.6245，能有效提高细胞核分割的准确性。
关键词： 注意力机制
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nuclear image segmentation based on improved maskr-cnn
abstract：the development of automatic nuclear segmentation, driven by the rise of deep learning, has significantly impacted the medical field. however, due to the problems of nuclear aggregation, adhesion, shape difference and low contrast, it is still difficult to capture effective features and blur edges. for the sake of solving the above problems, this paper proposes an enhanced maskr-cnn framework combined with attention mechanism (ca-maskr-cnn) for segmenting nuclear images, which uses separate resnet to increase the dimension of network cardinality to improve the ability of network semantic extraction, and introduces coordinate attention mechanism to pay attention to spatial information to improve the performance of network in nuclear image recognnuclear image segmentation based on improved maskr-cnnition. through the improved softening non-maximal inhibition screening, the detection effect of clusters and overlapping nuclei is improved, and a more appropriate loss function is selected to enhance the efficacy of nuclear segmentation. the experimental outcomes demonstrate that the f1 score index (f1-score) and superposition jacquard index (aji) of ca-maskr-cnn are 0.8454 and 0.6245 respectively, which can effectively improve the accuracy of nuclear segmentation.
keywords： attention mechanism
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基于改进mask r-cnn的细胞核图像分割方法
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