Biology subjects

Toulkeridou, E.

Publications and source records attributed to Toulkeridou, E..

3 recordsLinked to original sources

bioRxiv2026

Robust Trachea Segmentation from CT Imaging Using Fully Automated and Prompt-Based Models

Accurate trachea segmentation from computed tomography (CT) is a prerequisite for image-guided airway assessment, toward precision tracheostomy planning and safe endotracheal tube placement. Trachea-specific delineation remains challenging due to elongated geometry, small cross-sectional area, partial-volume effects, motion-trigerred artifacts, and heterogeneous surrounding tissues. Here, we study two complementary segmentation paradigms: a fully automated, self-configuring baseline (nnU-Net) and a prompt-conditioned foundation model (Med-SAM) derived from the Segment Anything Model (SAM). We evaluate both approaches on two heterogeneous regimes: (i) a spatially consistent volumetric dataset (AeroPath) and (ii) a large-scale, slice-based dataset without reliable volumetric continuity (OSIC). We further propose a hybrid inference strategy that enables fully automated prompting by deriving bounding-box prompts for MedSAM from coarse nnU-Net predictions. Results show that dataset structure critically influences reliability, interpretability, and deployment feasibility: volumetric continuity benefits fully automatic segmentation, while prompt-conditioned inference improves ROI-constrained interpretability in slice-based settings but introduces prompt sensitivity. We discuss limitations and outline clinically grounded evaluation and EHR integration directions for precision airway management.

bioengineering↗

bioRxiv2025

High-throughput phenomics of global ant biodiversity

The big data era in biology is underway, but the study of organismal form has been slow to capitalize on advances in imaging and computation. Modern imaging can digitize whole organisms, but low throughput has limited the effort to document morphological diversity. Within the open science initiative Antscan, we applied high-throughput synchrotron X-ray microtomography to capture phenotypes across a diverse and ecologically dominant insect group -- ants. We provide 2193 whole-body 3D ant datasets from 792 species to broadly cover the ant phylogeny with a global scope, also pairing phenomic data with genome sequencing projects. Scans acquired with standardized parameters facilitate automated analysis and free access to data can broaden the audience and incentivize methods development. Antscan presents a scalable approach to create libraries of diverse anatomies, heralding a new era of studies on the evolution, structure, and function of organismal phenotypes.

zoology↗

bioRxiv2021

Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 94 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggest that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy.

evolutionary biology↗