Abstract
An accurate steatosis quantification with pathology tissue samples is of high clinical importance. However, such pathology measurement is manually made in most clinical practices, subject to severe reader variability due to large sampling bias and poor reproducibility. Although some computerized automated methods are developed to quantify the steatosis regions, they present limited analysis capacity for high resolution whole-slide microscopy images and accurate overlapped steatosis division. In this paper, we propose a method that extracts an individual whole tissue piece at high resolution with minimum background area by estimating tissue bounding box and rotation angle. This is followed by the segmentation and segregation of steatosis regions with high curvature point detection and an ellipse fitting quality assessment method. We validate our method with isolated and overlapped steatosis regions in liver tissue images of 11 patients. The experimental results suggest that our method is promising for enhanced support of steatosis quantization during the pathology review for liver disease treatment.
Abstract (translated)
准确的脂肪变性定量与病理组织样本具有很高的临床重要性。然而,这种病理学测量是在大多数临床实践中手工制作的,由于大的取样偏差和重复性差,受到严重的读者差异。尽管开发了一些计算机化的自动化方法来量化脂肪变性区域,但是它们对于高分辨率全幻灯片显微镜图像和精确重叠的脂肪变性分割呈现有限的分析能力。在本文中,我们提出了一种方法,通过估计组织边界框和旋转角度,以最小背景区域的高分辨率提取单个整个组织边界。接下来是具有高曲率点检测的脂肪变性区域的分割和分离以及椭圆拟合质量评估方法。我们验证了我们的方法在11例患者的肝组织图像中发现了孤立且重叠的脂肪变性区域。实验结果表明,我们的方法有望在肝病治疗的病理学评论期间增强对脂肪变性量化的支持。
URL
https://arxiv.org/abs/1806.09090