Background

The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists’ visual evaluations on liver histology specimens.

Method

The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis.

Results

The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods.

Conclusions

To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 s), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.

中文翻译：

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肝移植中肝脂肪变性的全自动定量评估。

背景

大和微囊性脂肪变性的存在是肝移植的主要危险因素之一。对脂肪变性百分比的准确评估对于确定肝移植物的可移植性至关重要，目前这是基于病理学家对肝脏组织学标本的视觉评估。

方法

这项研究的目的是开发和验证一种完全自动化的算法，称为HEPASS（HEPatic自适应脂肪变性分割），用于在数字肝脏组织学图像中检测微观和宏观脂肪变性。所提出的方法采用了混合深度学习框架，将自适应阈值的准确性与深度卷积神经网络的语义分割相结合。从所有白色区域开始，HEPASS算法能够检测脂质滴并将其分类为微脂肪变性或大脂肪变性。

结果

拟议的方法已开发并在来自77个肝脏供体的385苏木精和曙红（H＆E）染色图像上进行了测试。自动化结果与人工注释和为脂肪变性分割设计的九种最新技术进行了比较。在TEST集中，该算法的特征是脂肪变性定量的准确度为97.27％（平均误差为1.07％，最大平均误差为5.62％），并且优于所有比较方法。

结论

据我们所知，所提出的算法是第一个用于对H＆E染色的肝组织图像中的微小和宏观脂肪变性进行评估的全自动算法。该算法非常快（平均计算时间为0.72 s），为自动，定量和实时肝移植评估铺平了道路。