肾细胞癌全切片图像中感兴趣区域的识别
Identifying regions of interest in whole slide images of renal cell carcinoma
AI总结：
本文提出一种基于DRLBP纹理描述子和颜色变换的自动化系统，用于识别肾细胞癌全切片图像中的感兴趣区域，并结合SVM和迁移学习实现高效分类，最佳精确率达99.17%。
摘要：
组织病理学图像包含大量信息，这可能使诊断成为一项极其耗时且繁琐的任务。在本研究中，我们开发了一个完全自动化的系统，用于检测肾细胞癌（RCC）全切片图像（WSI）中的感兴趣区域（ROIs），以减少分析时间并帮助病理学家做出更准确的决策。所提出的方法基于一种高效的纹理描述子，称为主导旋转局部二值模式（DRLBP），并结合颜色变换，以揭示和利用显微高放大倍数水平下巨大的纹理变异性。由此，DRLBP保留了结构信息，并利用局部邻域中的幅值以获得更强的判别能力。为了对相关ROIs进行分类，在颜色通道上分别对WSI图像块进行特征提取以形成直方图。接下来，我们使用最频繁出现的模式作为特征选择步骤，以丢弃非信息性特征。对来自12张全切片图像的1800个肾癌图像块，比较和评估了不同分类器的性能。此外，图像数据集的小规模使得可以研究基于迁移学习的深度学习方法，通过使用深度特征和微调方法进行图像块分类。获得了较高的识别准确率，且分类器效率较高，使用SVM获得的最佳精确率为99.17%。此外，迁移学习模型表现良好，性能相当，使用ResNet-50获得的最高精确率达到98.50%。所提出方法的结果表明图像分类非常高效，并证明了在识别ROIs方面的有效性。本研究提出了一种自动系统，用于在全切片组织病理学图像中检测与肾癌诊断相关的感兴趣区域。
英文摘要：
The histopathological images contain a huge amount of information, which can make diagnosis an extremely timeconsuming and tedious task. In this study, we developed a completely automated system to detect regions of interest (ROIs) in whole slide images (WSI) of renal cell carcinoma (RCC), to reduce time analysis and assist pathologists in making more accurate decisions. The proposed approach is based on an efficient texture descriptor named dominant rotated local binary pattern (DRLBP) and color transformation to reveal and exploit the immense texture variability at the microscopic high magnifications level. Thereby, the DRLBPs retain the structural information and utilize the magnitude values in a local neighborhood for more discriminative power. For the classification of the relevant ROIs, feature extraction of WSIs patches was performed on the color channels separately to form the histograms. Next, we used the most frequently occurring patterns as a feature selection step to discard non-informative features. The performances of different classifiers on a set of 1800 kidney cancer patches originating from 12 whole slide images were compared and evaluated. Furthermore, the small size of the image dataset allows to investigate deep learning approach based on transfer learning for image patches classification by using deep features and fine-tuning methods. High recognition accuracy was obtained and the classifiers are efficient, the best precision result was 99.17% achieved with SVM. Moreover, transfer learning models perform well with comparable performance, and the highest precision using ResNet-50 reached 98.50%. The proposed approach results revealed a very efficient image classification and demonstrated efficacy in identifying ROIs. This study presents an automatic system to detect regions of interest relevant to the diagnosis of kidney cancer in whole slide histopathology images.
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