BEHSOF: Advanced Non-alcoholic fatty liver dataset with clinical metadata and ultrasound images for Deep learning Models

The liver is regarded as one of the essential organs in the body, playing a crucial role in digestion, nutrient absorption, and food processing. This indispensable organ is tasked with cleansing the blood that flows from the digestive tract. Additionally, the liver detoxifies harmful substances and metabolizes various medications. A key function of this organ involves intricate metabolic processes that transform food into energy. However, the liver's heightened sensitivity makes it susceptible to a variety of common ailments, underscoring the need for careful attention to its health.Among liver diseases, fatty liver stands out as the most prevalent, characterized by the buildup of fat within liver cells. This condition is particularly common among individuals who are overweight or have abdominal obesity. Specifically, non-alcoholic fatty liver disease (NAFLD) refers to the excessive fat accumulation in liver cells, a condition known as steatosis. Various diagnostic techniques have been developed for this disease, each offering distinct advantages and drawbacks. Ultrasound imaging has gained popularity due to its accessibility, non-invasive nature, and affordability. One of the approaches utilized in this diagnostic process is the employment of deep learning models. Sporadic studies have been conducted to leverage the available data in diagnosing the degree of non-alcoholic fatty liver disease, and given the significance of the issue, this body of research is continually expanding. Developing robust model training and conducting comprehensive analyses necessitates access to a diverse and comprehensive data repository that can be leveraged for training and validating the proposed models. To this end, we present the BEHSOF dataset, which comprises a collection of samples gathered with varying degrees of non-alcoholic fatty liver disease. Specifically, this data bank consists of ultrasound images from a population of 113 individuals under study, along with the corresponding labels for the levels of steatosis and fibrosis. In addition to the ultrasound images, the data bank provides clinical information, blood test results, and Fibroscan outcomes for the participants, which serve as reference data for fibrosis assessment. Finally, we showcase the results of two deep learning models as examples for training and testing the introduced data set.The ultrasound images have been categorized into two distinct groups based on the diagnostic findings and labeling conventions. Each file within these categories is further distinguished by the location of the medical facility (Taleghani Hospital (TAL) or Behbood Clinic (BEH)), the row number, and the order of grading (steatosis score by expert, steatosis score by CAPscore, fibrosis score by the rate of Elasticity) (TALXXXXX, BEHXXXXX).

肝脏被视为人体至关重要的器官之一，在消化、营养吸收与食物代谢过程中发挥着关键作用。这一不可或缺的器官负责清除来自消化道的血液，此外还能解毒有害物质、代谢各类药物。该器官的核心功能之一是通过复杂的代谢过程将食物转化为能量。然而，肝脏的敏感性较高，易受多种常见疾病侵袭，这凸显了关注肝脏健康的必要性。在各类肝脏疾病中，脂肪肝最为普遍，其特征为肝细胞内脂肪堆积。该病症在超重或腹型肥胖人群中尤为常见。具体而言，非酒精性脂肪性肝病（NAFLD）指肝细胞内存在过量脂肪堆积的情况，这一病症也被称为脂肪变性（steatosis）。目前已开发出多种针对该疾病的诊断技术，各有其优缺点。超声成像（Ultrasound imaging）凭借其便捷性、无创性与成本低廉的优势得到了广泛应用。在此类诊断流程中，深度学习模型（deep learning models）是常用的技术手段之一。已有零星研究尝试利用现有数据对非酒精性脂肪性肝病的严重程度进行诊断，鉴于该问题的重要性，相关研究正不断拓展。要开发鲁棒的模型训练方案并开展全面分析，需要获取多样化且全面的数据集资源，用于训练与验证所提出的模型。为此，我们构建并发布了BEHSOF数据集，该数据集包含了不同严重程度非酒精性脂肪性肝病的样本集合。具体而言，该数据集涵盖了113名研究对象的超声图像，以及对应的脂肪变性与纤维化程度标注标签。除超声图像外，该数据集还提供了研究对象的临床信息、血液检测结果以及Fibroscan检测结果，这些数据可作为纤维化评估的参考依据。最后，我们展示了两个深度学习模型的训练与测试结果，作为该数据集的应用示例。根据诊断结果与标注规范，超声图像已被划分为两个不同组别。每个类别下的文件还可通过医疗机构信息（塔利加尼医院（Taleghani Hospital, TAL）或贝赫布德诊所（Behbood Clinic, BEH））、行号以及分级顺序（专家评定的脂肪变性评分、CAP评分评定的脂肪变性评分、弹性值评定的纤维化评分）进行区分，文件名格式为TALXXXXX、BEHXXXXX。

搜集汇总

背景与挑战

背景概述

BEHSOF数据集是一个包含113名非酒精性脂肪肝患者超声图像、临床信息和血液检测结果的数据集，旨在为深度学习模型的训练和验证提供支持。数据集还包含Fibroscan结果作为纤维化评估的参考数据。