Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.

Authors

Kartik J Deopujari, Matthias Schmal, Caroline Danner, Zainab Abdul Qayyum, Jordy T Zwerus, Julian Kopp, Mihail Besleaga, Roghayeh Shirvani, Astrid R Mach-Aigner, Robert L Mach, Christian Zimmermann

Published in

Fungal biology and biotechnology. Volume 13. Issue 1. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observation remains a widely used approach to study these phenotypes, but variation in sample preparation and operator skill can limit the scale of sample processing or introduce operator bias. Although several image-based cell detection tools have been developed, most are tailored to specific applications or limited to a particular taxon. To address the need for a tool applicable to the polymorphic, yeast-like fungus Aureobasidium pullulans, and with potential applicability to other taxa, we developed TU_MyCo-Vision, an Ultralytics YOLO (You Only Look Once) based object detection tool for identifying 13 fungal morphotypes in bright-field microscopic images. Identification of 13 fungal morphotypes, including variation of vacuolated single cells, cells with granular cytoplasmic appearance, and diverse hyphal forms, is achieved by integrating a YOLOv11m-based object detector trained on a custom dataset of 1,504 annotated images and a standalone graphical user interface that enables downstream data analysis and visualization of results. The best-performing model (Zulu_s3) achieved a mean precision of 73.4%, a recall of 66.5%, a mean average precision at 50% IoU (mAP@50) of 73.5%, and a mean average precision at varying IoU thresholds between 50 and 90% IoU (mAP@50-95) of 54.5% across all 13 classes. The single-group analysis pipeline was validated on a 90-image test set, generating six quantitative summaries that capture the distribution and co-occurrence of fungal morphotypes, including absolute counts, relative and mean relative abundance plots, stacked bar plots and clustered heatmaps. Multi-group evaluation on previously unseen datasets comprising Candida albicans, Komagataella phaffi, and Aspergillus niger spores demonstrated that these morphotype profiles can be compared across biologically distinct genera, highlighting the tool's potential applicability for studying fungal morphological diversity.

PMID:
42581378
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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