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Related Experiment Video

Updated: Jan 7, 2026

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body
08:08

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

Published on: January 11, 2018

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CsDETC: detection and counting of small target Cryptococcus spp.

Yanhui Chen1, Yiwen Luo2, Zan Yang2

  • 1Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Frontiers in Cellular and Infection Microbiology
|January 1, 2026
PubMed
Summary

A new method, CsDETC, accurately detects and counts Cryptococcus spp. in cerebrospinal fluid, improving diagnosis of cryptococcal meningitis and pulmonary infections. This automated approach enhances clinical efficiency and patient outcomes.

Keywords:
Cryptococcus spp. detectionattention-enhanced path aggregation networkcountingdiagnostic efficiencyhypergraph computation

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Area of Science:

  • Medical Diagnostics
  • Computer Vision
  • Infectious Diseases

Background:

  • Cryptococcus spp. infections cause serious conditions like cryptococcal meningitis (CM).
  • Current diagnostic methods (CSF ink staining, culture) are manual, inefficient, and lack accuracy.
  • Accurate and timely diagnosis of Cryptococcus spp. is crucial for effective clinical management.

Purpose of the Study:

  • To develop an automated method for detecting and counting Cryptococcus spp. in cerebrospinal fluid (CSF).
  • To improve the accuracy and efficiency of diagnosing Cryptococcus spp. infections.

Main Methods:

  • Proposed CsDETC method integrating data augmentation, hypergraph computation (HGC-SCS), and attention-enhanced path aggregation network (AEPAN).
  • Extensive data augmentation (cropping, flipping, rotation, perspective transformation) enhanced model generalization.
  • Integrated CBAM into PAN for improved small target perception through channel and spatial attention.

Main Results:

  • CsDETC demonstrated superior performance over advanced models like YOLOv10 and YOLO11 on a private dataset.
  • Achieved significant improvements in mAP50 (93.6% vs. 91.3%), APs (51.0% vs. 49.5%), and MAE (1.865 vs. 2.370).
  • Inference time increased minimally (0.7 ms).

Conclusions:

  • CsDETC shows promise as a tool for accurate and timely identification and automated counting of Cryptococcus spp.
  • Further validation with larger, diverse datasets is needed.
  • Potential to aid clinicians in treatment planning and enhance diagnostic efficiency for cryptococcosis.