Accurate polyp segmentation is of great significance for the diagnosis and treatment of colon cancer. Deep convolution network can extract the common high level features of the target. However, most network models ignore some individual features, so the sample prediction results in complex space are fuzzy and lack of regularity. In this paper, a coarse-to-fine segmentation frame for polyp segmentation via deep and classification features is proposed. Firstly, batch schatten-p norms maximization is introduced into a network model to strengthen the predict map. Then, an automatic two classification mechanism is constructed and the prediction map is classified into two categories: simple and complex samples. Since the CNN prediction maps of simple samples are close to binary images, the prediction maps are not processed. Finally, an active contour model segmentation algorithm for saliency detection of complex samples is proposed. Experiments on Kvasir-SEG, CVC-300, CVC-ClinincDB, CVC-ColonDB and ETIS-LaribPolypDB datasets using multiple models verify the effectiveness of the framework. Code is available at https://doi.org/10.24433/CO.7821162.v1.

准确的息肉分割对于结肠癌的诊断和治疗具有重要意义。深度卷积网络可以提取目标的常见高级特征。然而，大多数网络模型忽略了一些个体特征，因此复杂空间中的样本预测结果模糊，缺乏规律性。在本文中，提出了一种通过深度和分类特征进行息肉分割的粗到细分割框架。首先，将批量 schatten-p 范数最大化引入网络模型以加强预测图。然后，构建自动二分类机制，将预测图分为简单样本和复杂样本两类。由于简单样本的CNN预测图接近二值图像，所以预测图没有被处理。最后，提出了一种用于复杂样本显着性检测的主动轮廓模型分割算法。使用多个模型在 Kvasir-SEG、CVC-300、CVC-ClinincDB、CVC-ColonDB 和 ETIS-LaribPolypDB 数据集上的实验验证了框架的有效性。代码可在 https://doi.org/10.24433/CO.7821162.v1 获得。