HEPASS algorithm dataset

This repository contains the image dataset and the manual annotations used to develop the HEPASS algorithm for automated liver steatosis quantification: - Salvi M., Molinaro M., Metovic J., Patrono D., Romagnoli R., Papotti M, and Molinari F., "Fully Automated Quantitative Assessment of Hepatic Steatosis in Liver Transplants", Computers in Biology and Medicine 2020 (DOI: 10.​1016/​j.​compbiomed.​2020.​103836) ABSTRACT 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 seconds), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.本仓库包含用于开发HEPASS（HEPatic Adaptive Steatosis Segmentation，肝脏自适应脂肪变性分割）算法的图像数据集与人工标注数据，该算法用于自动化肝脏脂肪变性定量分析：Salvi M.、Molinaro M.、Metovic J.、Patrono D.、Romagnoli R.、Papotti M及Molinari F. 于2020年发表在《Computers in Biology and Medicine》的论文，原文标题为《Fully Automated Quantitative Assessment of Hepatic Steatosis in Liver Transplants》，DOI: 10.1016/j.compbiomed.2020.103836。 摘要 背景：大泡性与小泡性脂肪变性是肝移植的主要风险因素之一。当前评估肝脏移植物可移植性的核心依据为病理学家对肝脏组织学标本的视觉评估，准确量化脂肪变性占比至关重要。 方法：本研究旨在开发并验证一款名为HEPASS的全自动算法，用于检测数字化肝脏组织学图像中的小泡性与大泡性脂肪变性。所提方法采用混合深度学习框架，将自适应阈值的精准性与深度卷积神经网络的语义分割能力相结合。HEPASS算法以所有白色区域为起始点，可检测脂滴并将其分类为小泡性或大泡性脂肪变性。 结果：本方法基于77名肝脏供体的385张苏木精-伊红（hematoxylin and eosin，H&E）染色图像开发并测试。将自动分析结果与人工标注以及9种当前主流的脂肪变性分割技术进行对比。在测试集上，该算法的脂肪变性定量准确率达97.27%（平均误差1.07%，最大平均误差5.62%），性能优于所有对比方法。 结论：据我们所知，本算法是首款可同时评估H&E染色肝脏组织图像中小泡性与大泡性脂肪变性的全自动算法。该算法运算速度极快（平均运算耗时0.72秒），为实现自动化、定量且实时的肝脏移植物评估铺平了道路。