Clinical urine microscopy for urinary tract infections

Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files  Objects  Count Pixel Values

img 300 Raw data 0-255
bin_mask  300 Background/Foreground 0/1
mult_mask  300 Background/Class 0
Rod 1697 1
RBC/WBC 1056 2
Yeast 41 3
Miscellaneous  550 4
Single EPC 182 5
Small EPC sheet 26 6
Large EPC sheet  10 7

Total 3562

尿路感染（Urinary Tract Infection, UTI）是一种常见疾病，其诊断可通过对排出尿液进行显微镜检查，检测感染相关细胞标志物实现。本文构建了一个包含300张图像与3562个经人工标注的尿路细胞的数据集，所有细胞被划分为7类具有临床意义的尿路成分。该数据集为富集型数据集，样本取自有症状尿路感染患者的未染色、未处理尿液。本数据集旨在依托先进机器学习技术与简易成像系统，助力几乎所有临床场景下的尿路感染诊断。

数据采集：2022年4月至8月，研究团队从伦敦市中心一家专科下尿路症状（Lower Urinary Tract Symptoms, LUTS）门诊的有症状尿路感染患者中，获取300份尿液样本。所有尿液样本均为自然排出的标本，且在采集后1小时内完成现场处理，以减缓细胞降解。采用明场显微镜（brightfield microscope）进行检查，具体配置为：奥林巴斯BX41F显微镜机架、U-5RE五筒物镜转换器、U-LS30 LED光源、U-AC阿贝聚光镜，搭配20倍物镜（奥林巴斯PLCN20x Plan C N Achromat 20x/0.4）。使用一次性血细胞计数板（C Chip™），由两名经验丰富的显微镜技师对每微升尿液中的红细胞（Red Blood Cell, RBC）、白细胞（White Blood Cell, WBC）、上皮细胞（Epithelial Cell, EPC）及其他细胞成分进行计数。图像采集采用前述明场显微镜，搭配0.5倍C型转接器与数码彩色相机（Infinity 3S-1UR，Teledyne Lumenera），以16位彩色格式采集，分辨率为1392×1040，文件格式为.tif，通过Capture and Analyse软件完成拍摄。为最大化采集图像中的尿路细胞占比，本数据集采用富集构建策略；该数据整理方式同时可有效缓解类别不平衡问题。图像采集过程中每日进行科勒照明校准与全局白平衡调整，以保证成像一致性。

数据集标注：首先通过二分类语义分割任务识别目标细胞，完成300张图像的手动标注。图像像素被二分类标记为有效细胞（前景）或无效背景，其中无效背景还包含无法识别的细胞碎片、严重失焦颗粒等。标注初期使用ilastik——一款基于随机森林分类器实现像素分类的开源软件——完成二分类标注，随后逐像素手动修正，以确保语义分割的准确性。由此为每张原始彩色图像生成对应分辨率为1392×1040的.tif格式二值掩码。随后由两名资深显微镜技师将目标对象手动标注为7类具有临床意义的多分类类别：杆菌、红细胞/白细胞、酵母菌、杂类、单个上皮细胞、小上皮细胞团、大上皮细胞团。由此生成分辨率为1392×1040的.tif格式多分类掩码，像素值0~7对应不同类别，其中0代表背景（详见表1）。

数据结构：本数据集分为三个根文件夹：img（图像）、bin_mask（二值掩码）与mult_mask（多分类掩码）。每个文件夹均包含300个.tif格式文件，以递增数字命名。

表1 文件夹 文件数 目标对象 计数 像素值
img 300 原始数据 — 0~255
bin_mask 300 背景/前景 — 0/1
mult_mask 300 背景/类别 — 0
— 杆菌 1697 1
— 红细胞/白细胞 1056 2
— 酵母菌 41 3
— 杂类 550 4
— 单个上皮细胞 182 5
— 小上皮细胞团 26 6
— 大上皮细胞团 10 7
总计 — — 3562 —

搜集汇总

背景与挑战

背景概述

该数据集是一个用于尿路感染（UTI）诊断的临床尿液显微镜图像数据集，包含300张图像和3,562个手动标注的尿液细胞，分为七个临床相关类别（如杆状菌、红细胞/白细胞等）。数据采集自有症状UTI患者的尿液样本，采用显微镜和数字相机获取高质量图像，并进行了二进制和多类语义分割标注，旨在通过机器学习技术辅助UTI诊断，适用于各种临床环境。