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首页> 外文期刊>International journal of imaging systems and technology >Multi-feature fusion of deep networks for mitosis segmentation in histological images
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Multi-feature fusion of deep networks for mitosis segmentation in histological images

机译：组织学图像中有丝分裂细分的深网络多重特征融合

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Mitotic cell detection in pathological images is significant for predicting the malignancy of tumors and the intelligent segmentation of these cells. Overcoming human error generated by pathologists in reading the images while enabling fast detection through high computing power remains a very challenging task. In this study, we proposed a method that fuses handcrafted features and deep features to segment mitotic cells in whole-slide images. The handcrafted feature extraction strategy was based on four measure indices of the Gray Level Co-occurrence Matrix. The deep feature extraction strategy was based on natural image knowledge transfer. Finally, the two strategies were fused to classify and distinguish the image pixels for the segmentation of mitotic cells. We used the AMIDA13 dataset and the pathological images collected by the Department of Pathology of Anhui No. 2 Provincial People's Hospital as the experimental dataset. We compared the Areas Under Curve (AUC) of Receiver Operating Characteristic obtained through the handcrafted feature model, the improved deep feature model with knowledge transfer, the classic U-NET model, and the proposed multi-feature fusion model. The results showed that the AUC values of our proposed method had 0.07 and 0.05 improved to classic U-NET model on test dataset and validation dataset respectively, while achieved the best segmentation performance and detected most of true-positive cells, representing a breakthrough for clinical application. The experiments also indicated that the staining uniformity of pathological tissue impacted the model performance.
机译：病理图像中的有丝分裂细胞检测对于预测肿瘤恶性和这些细胞的智能分割是显着的。克服了病理学家在读取图像时产生的人为错误，同时通过高计算能力启用快速检测仍然是一个非常具有挑战性的任务。在这项研究中，我们提出了一种方法，该方法将手工制作的特征和深度特征融合在整个滑动图像中的分段有丝分裂细胞。手工制作特征提取策略基于灰度共发生矩阵的四个测量指标。深度特征提取策略基于自然图像知识转移。最后，融合了两种策略以分类和区分丝分裂细胞的分割的图像像素。我们使用了Amida13数据集和安徽省省级人民医院病理学部收集的病理图像作为实验数据集。我们比较了通过手工特征模型获得的接收器操作特性的曲线（AUC）的区域，改进的深度特征模型，具有知识传输，经典U-Net模型和所提出的多特征融合模型。结果表明，我们所提出的方法的AUC值分别对测试数据集和验证数据集的经典U-净模型有0.07和0.05，同时达到了最佳的分段性能，并检测到大部分真正的细胞，代表临床突破应用。实验还表明，病理组织的染色均匀性影响了模型性能。

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