A dataset of microscopic spirometra mansoni for medical image segmentation

Sirui Chen; Yan Liu; Qian Zheng

doi:10.1016/j.compbiomed.2025.110661

A dataset of microscopic spirometra mansoni for medical image segmentation

Comput Biol Med. 2025 Jun 26:195:110661. doi: 10.1016/j.compbiomed.2025.110661. Online ahead of print.

Authors

Sirui Chen¹, Yan Liu², Qian Zheng³

Affiliations

¹ Hengyang Medical College, University of South China, Hengyang, Hunan, China; Mechanical and Electronic Information College, China University of Geosciences, Wuhan, Hubei, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, Xinjiang, China. Electronic address: chenhy0826@foxmail.com.
² Hengyang Medical College, University of South China, Hengyang, Hunan, China; School of nursing, University of South China, Hengyang, Hunan, China. Electronic address: 453611453@qq.com.
³ The first affiliated hospital of ShanDong First Medical University Shan Dong provincial qianfoshan hospital, JiNan, Shandong Prov, China. Electronic address: pupil7@126.com.

PMID: 40578156
DOI: 10.1016/j.compbiomed.2025.110661

Abstract

Accurate diagnosis of adult Spirometra mansoni infections remains challenging, due to the limited sensitivity and high cost associated with immunodiagnostic methods. Advances in computer vision suggest deep learning-based etiological image analysis could overcome these limitations. A barrier persists, however, in current computational parasitology research: the shortage of available datasets for training convolutional neural networks (CNNs) in adult S. mansoni identification. We present a microscopic imaging dataset specifically optimized for developing and validating semantic segmentation CNNs in S. mansoni detection to address this issue. This resource comprises 1420 microscopic images derived from fecal suspensions of confirmed infection cases, captured through standardized protocols using a Nikon BM-1000 optical microscope. All specimens were expert-annotated using LabelMe to generate segmentation masks delineating egg boundaries and distinctive morphological features. Furthermore, we conducted quantitative analyses on this dataset, including assessments of background entropy and the morphological entropy of individual S. mansoni eggs, along with investigation into their correlation. We evaluated segmentation architectures using this dataset to establish performance benchmarks, providing baselines for future methodological improvements. Spirometra Mansoni Dataset (SMD) is publicly available at https://www.heywhale.com/mw/dataset/684ff79484f1ba451ba76660/content under a CC BY 4.0 license.

Keywords: Dataset; Semantic segmentation; Spirometra mansoni.