Medical image segmentation is an essential task in computer-aided diagnosis. Despite their prevalence and success, deep convolutional neural networks (DCNNs) still need to be improved to produce accurate and robust enough segmentation results for clinical use. In this paper, we propose a novel and generic framework called Segmentation-Emendation-reSegmentation-Verification (SESV) to improve the accuracy of existing DCNNs in medical image segmentation, instead of designing a more accurate segmentation model. Our idea is to predict the segmentation errors produced by an existing model and then correct them. Since predicting segmentation errors is challenging, we design two ways to tolerate the mistakes in the error prediction. First, rather than using a predicted segmentation error map to correct the segmentation mask directly, we only treat the error map as the prior that indicates the locations where segmentation errors are prone to occur, and then concatenate the error map with the image and segmentation mask as the input of a re-segmentation network. Second, we introduce a verification network to determine whether to accept or reject the refined mask produced by the re-segmentation network on a region-by-region basis. The experimental results on the CRAG, ISIC, and IDRiD datasets suggest that using our SESV framework can improve the accuracy of DeepLabv3+ substantially and achieve advanced performance in the segmentation of gland cells, skin lesions, and retinal microaneurysms. Consistent conclusions can also be drawn when using PSPNet, U-Net, and FPN as the segmentation network, respectively. Therefore, our SESV framework is capable of improving the accuracy of different DCNNs on different medical image segmentation tasks.

中文翻译：

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SESV：通过预测和纠正错误进行准确的医学图像分割。

医学图像分割是计算机辅助诊断中的重要任务。尽管已普及并取得了成功，但深度卷积神经网络（DCNN）仍需要改进以产生足够准确和鲁棒的分割结果，以供临床使用。在本文中，我们提出了一种新颖且通用的框架，称为“分段-修正-重新分段-验证”（SESV），以提高医学图像分割中现有DCNN的准确性，而不是设计一个更准确的分割模型。我们的想法是预测现有模型产生的细分误差，然后对其进行纠正。由于预测细分错误具有挑战性，因此我们设计了两种方法来容忍错误预测中的错误。首先，与其使用预测的细分误差图直接校正细分蒙版，我们仅将错误图视为先验信息，指出容易发生分段错误的位置，然后将错误图与图像和分段掩码连接起来作为重新分段网络的输入。其次，我们引入了一个验证网络，以确定是否要接受或拒绝由重新分段网络在逐个区域的基础上生成的精制蒙版。在CRAG，ISIC和IDRiD数据集上的实验结果表明，使用我们的SESV框架可以大大提高DeepLabv3 +的准确性，并在腺细胞，皮肤病变和视网膜微动脉瘤的分割方面取得先进的性能。当分别使用PSPNet，U-Net和FPN作为分割网络时，也可以得出一致的结论。因此，