DMID-ESD: A Benchmark Darkfield Microscopy Image Dataset for Erythrocytes and Spirochaete Detection and Classification

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2025-09-11 DOI:10.1016/j.bspc.2025.108595

Guotao Lu , Zizhen Fan , Minghe Gao , Jing Chen , Qingtao Meng , Hechen Yang , Hongzan Sun , Tao Jiang , Yudong Yao , Marcin Grzegorzek , Chen Li

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引用次数: 0

Abstract

Since the analysis of erythrocytes and spirochaetes is highly relevant to human health, their automated detection is of significant importance in both medical and computer vision research. However, publicly available datasets in this domain remain scarce. To address this gap, we present the Darkfield Microscopy Image Dataset for Erythrocytes and Spirochaete Detection (DMID-ESD), which consists of 11,794 fully annotated images with labels containing categorical and localization information. We perform comprehensive benchmarking experiments on DMID-ESD to evaluate its utility in tasks such as image classification, object detection, and feature extraction. The results demonstrate that the dataset serves as an effective benchmark for method evaluation. The DMID-ESD dataset is freely available for non-commercial use at: https://figshare.com/articles/dataset/DMID-ESD_zip/22179311.

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DMID-ESD：用于红细胞和螺旋体检测和分类的基准暗场显微镜图像数据集

由于红细胞和螺旋体的分析与人类健康高度相关，因此它们的自动检测在医学和计算机视觉研究中都具有重要意义。然而，在这一领域公开可用的数据集仍然很少。为了解决这一问题，我们提出了用于红细胞和螺旋菌检测的暗场显微镜图像数据集（DMID-ESD），该数据集由11,794张完全注释的图像组成，其标签包含分类和定位信息。我们对DMID-ESD进行了全面的基准实验，以评估其在图像分类，目标检测和特征提取等任务中的实用性。结果表明，该数据集可作为方法评价的有效基准。DMID-ESD数据集可免费用于非商业用途：https://figshare.com/articles/dataset/DMID-ESD_zip/22179311。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.