Salzburg University of Applied Sciences, Salzburg, Austria; Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany.
Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany.
Comput Med Imaging Graph. 2019 Jan;71:40-48. doi: 10.1016/j.compmedimag.2018.11.002. Epub 2018 Nov 16.
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, large amounts of digital image data are being generated. Accordingly, there is a strong demand for the development of computer based image analysis systems. Here, we address application scenarios in histopathology consisting of sparse, small objects-of-interest occurring in the large gigapixel images. To tackle the thereby arising challenges, we propose two different CNN cascade approaches which are subsequently applied to segment the glomeruli in whole slide images of the kidney and compared with conventional fully-convolutional networks. To facilitate unbiased evaluation, eight-fold cross-validation is performed and finally means and standard deviations are reported. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained (precision: 0.89, recall: 0.92). Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to previous approaches. We can state that especially one of the proposed cascade networks proved to be a highly powerful tool providing the best segmentation accuracies and also keeping the computing time at the lowest level. This work facilitates accurate automated segmentation of renal whole slide images which consequently allows fully-automated big data analyses for the assessment of medical treatments. Furthermore, this approach can also easily be adapted to other similar biomedical application scenarios.
由于全切片扫描仪的普及，使得组织的病理数字化变得更加容易，大量的数字图像数据应运而生。因此，人们强烈要求开发基于计算机的图像分析系统。在这里，我们解决了组织病理学中的应用场景，这些场景由稀疏的、小的感兴趣目标组成，存在于大型千兆像素图像中。为了解决由此产生的挑战，我们提出了两种不同的 CNN 级联方法，随后将其应用于肾脏全切片图像中的肾小球分割，并与传统的全卷积网络进行比较。为了便于进行无偏评估，我们进行了 8 倍交叉验证，最终报告了平均值和标准差。总体而言，在性能最佳的级联方法中，单个 CNN 的表现不如级联方法，像素级 Dice 相似系数达到 0.90（精度：0.89，召回率：0.92）。结合定性和进一步的对象级分析，与以前的方法相比，获得的结果也被评估为优秀。我们可以说，特别是所提出的级联网络之一被证明是一种非常强大的工具，它提供了最佳的分割精度，同时将计算时间保持在最低水平。这项工作促进了肾脏全切片图像的自动精确分割，从而可以对医疗治疗进行全自动大数据分析。此外，这种方法也可以很容易地适应其他类似的生物医学应用场景。
