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摘要: 精准的医学图像自动分割是临床影像学诊断和影像三维重建的重要基础.但医学图像数据的目标对象间对比度差异小、受器官运动影响大, 加之标注样本规模小, 因此在小样本下建立高性能的医学分割模型仍是目前的难点问题. 针对主流原型学习小样本分割网络对医学图像边界分割性能差的问题, 提出一种迭代边界优化的小样本分割网络(Iterative boundary refinement based few-shot-segmentation network, IBR-FSS-Net). 以双分支原型学习的小样本分割框架为基础引入类别注意力机制和密集比较模块, 对粗分割掩码进行迭代优化, 引导分割模型在多次迭代学习过程中关注边界, 从而提升边界分割精度. 为进一步克服医学图像训练样本少且多样性不足的问题, 使用超像素方法生成伪标签, 扩充训练数据以提升模型泛化性. 在主流的ABD-MR和ABD-CT医学图像分割公共数据集上进行实验, 与现有多种先进的医学图像小样本分割方法进行了广泛的对比分析和消融实验. 结果表明, 该方法有效提升了未见医学类别的分割性能.Abstract: Accurate automatic segmentation of medical images is an important basis for clinical imaging diagnosis and 3D image reconstruction. However, medical image data has small contrast differences between target objects, is greatly affected by organ movement, and the scale of labeled samples is small. Therefore, it is still a difficult problem to establish a high-performance medical segmentation model under few samples. In view of the poor performance of the mainstream prototype learning few-shot segmentation network for medical image boundary segmentation, an iterative boundary refinement few-shot segmentation network (IBR-FSS-Net) is proposed.Based on the few-shot segmentation framework of dual-branch prototype learning, the category attention mechanism and dense comparison module are introduced to iteratively refine the coarse segmentation mask, and guide the segmentation model to focus on the boundary during multiple iterative learning processes, thereby improving the boundary segmentation accuracy. In order to further overcome the problem of few training samples and insufficient diversity of medical images, this paper uses the superpixel method to generate pseudo-labels and expand the training data to improve the generalization of the model. Experiments on the mainstream ABD-MR and ABD-CT medical image segmentation public datasets is done, and conducts extensive comparative analysis and ablation experiments with various existing advanced medical image few-shot segmentation methods. The results show that this method effectively improves the segmentation performance of unseen medical categories.1) 本文责任编委 XXX Recommended by Associate Editor BIAN Wei
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表 1 在 ABD-CT 与 ABD-MR 数据集上不同模型的量化分割结果(%)
Table 1 Quantitative segmentation results of different models on ABD-CT and ABD-MR datasets (%)
方法 ABD-CT数据集 ABD-MR数据集 脾脏 左肾 右肾 肝脏 平均值 脾脏 左肾 右肾 肝脏 平均值 SE-Net 0.23 32.83 14.34 0.27 11.91 51.80 62.11 61.32 27.43 50.66 PANet 25.59 32.34 17.37 38.42 29.42 50.90 53.45 38.64 42.26 46.33 ALPNet 60.25 63.34 54.82 73.65 63.02 67.02 73.63 78.39 73.05 73.02 GCN-DE 56.53 68.13 75.50 46.77 61.73 60.63 76.07 83.03 49.47 67.30 RP-Net 69.85 70.48 70.00 79.62 72.48 76.35 81.40 85.78 73.51 79.26 ADNet — — — — — 75.92 75.28 83.28 80.81 78.82 PoissonSeg 52.33 50.11 47.02 58.74 52.05 52.85 50.58 53.57 61.03 54.51 AAS-DCL 66.36 64.71 69.95 71.61 68.16 74.86 76.90 83.75 69.94 76.36 IBR-FSS-Net 71.73 73.78 72.02 78.13 73.92 75.12 82.19 85.64 75.89 79.71 表 2 不同组件的量化分割结果(%)
Table 2 Quantified segmentation results for different components (%)
组件 脾脏 左肾 右肾 肝脏 平均值 Baseline 62.33 63.65 66.87 64.18 64.26 Baseline+Concat 60.62 65.55 68.53 66.56 65.32 Baseline+BRM 66.63 72.10 74.83 69.17 70.68 Baseline+3Concat 61.20 68.72 70.38 66.95 66.81 Baseline+3BRM 75.12 82.19 85.64 75.89 79.71 表 3 边界优化模块的数量的影响(%)
Table 3 The effect of the number of boundary refinement modules (%)
组件 脾脏 左肾 右肾 肝脏 平均值 Baseline 62.33 63.65 66.87 64.18 64.26 Baseline+BRM 66.63 72.10 74.83 69.17 70.68 Baseline+2BRM 69.88 79.98 82.12 73.56 76.39 Baseline+3BRM 75.12 82.19 85.64 75.89 79.71 Baseline+4BRM 68.57 77.23 78.82 69.60 73.56 Baseline+5BRM 64.13 70.55 72.69 66.42 68.45 表 4 不同特征提取网络的量化分割结果(%)
Table 4 Quantified segmentation results of different feature extraction networks (%)
骨干网络 脾脏 左肾 右肾 肝脏 平均值 VGG-16 52.09 63.83 64.48 57.88 59.57 U-Net 69.66 78.94 80.46 72.15 75.30 Res U-Net 71.82 78.24 81.10 73.41 76.14 Attention U-Net 73.96 79.14 83.51 73.60 77.55 ResNet-50 71.23 78.19 82.57 73.68 76.42 ResNet-101 75.12 82.19 85.64 75.89 79.71 表 5 度量网络组合方式的影响(%)
Table 5 The effect of the combination of metric network (%)
原型网络拼接方式 脾脏 左肾 右肾 肝脏 平均值 Prototypical-Net 70.92 80.61 83.70 74.48 77.43 DCM 71.53 81.36 83.44 74.81 77.79 DCM + Prototypical-Net 72.97 81.49 83.68 74.83 78.24 Prototypical-Net +DCM 75.12 82.19 85.64 75.89 79.71 表 6 与使用半监督、对比学习、弱监督的医学分割模型在 ABD-CT 与 ABD-MR 数据集上结果对比(%)
Table 6 Comparison of results with medical segmentation models ssing semi-supervised, comparative learning, and weakly supervised on ABD-CT and ABD-MR datasets (%)
方法(比例) ABD-CT数据集 ABD-MR数据集 脾脏 左肾 右肾 肝脏 平均值 脾脏 左肾 右肾 肝脏 平均值 MagicNet(30%) 91.42 86.19 84.64 93.89 89.04 — — — — — CVCL(部分) 95.40 94.60 94.60 96.70 95.33 — — — — — C-CAM(0%) — — — — — 74.16 81.00 84.75 72.68 78.15 IBR-FSS-Net(5%) 71.73 73.78 72.02 78.13 73.92 75.12 82.19 85.64 75.89 79.71 -
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