Biology subjects

Toor, M.

Publications and source records attributed to Toor, M..

3 recordsLinked to original sources

bioRxiv2026

ZEISS arivis Cloud: a cloud-based platform for deep learning model training and scalable bioimage analysis

Modern biological imaging generates large, complex datasets that require scalable and reproducible image analysis methods. Deep learning has demonstrated strong performance on bioimage segmentation tasks, but training custom models has remained inaccessible to many researchers due to requirements for GPU infrastructure, programming expertise, and large annotated training datasets. ZEISS arivis Cloud is a browser-based platform for deep learning model training that addresses these barriers through partial annotation support, AI-assisted labeling with SAM (Segment Anything Model), pretrained model initialization, and automatically configured training pipelines requiring no machine learning expertise. The platform supports two segmentation tasks: semantic segmentation using a U-Net-style architecture with an EfficientNet encoder and PixelShuffle decoder, and instance segmentation based on Mask2Former with a Swin-Tiny backbone. Both pipelines incorporate microscopy-specific adaptations including smooth tiling, multi-channel input support, dataset-specific normalization, and partial-annotation-aware loss functions protected by patents US-20240078681-A1 and US-20250111519-A1. Trained models integrate directly with ZEISS arivis Pro for pipeline-based image analysis, ZEISS arivis Hub for parallel execution across large datasets, and ZEISS ZEN for content-aware guided acquisition. We describe the platform architecture, training methodology, segmentation architectures, reproducibility and versioning mechanisms, and FAIR compliance, and illustrate the complete workflow through two intestinal organoid imaging examples. arivis Cloud is freely accessible to student users; other users access the platform via subscription at https://www.arivis.cloud/.

bioinformatics↗

bioRxiv2025

Accessible AI Enhances Monitoring of Coral Seeding Devices in Reef Restoration

1. Coral seeding devices (CSDs) - tools designed to deliver sexually propagated corals to target locations - offer a promising means to increase coral abundance on degraded reefs. However, evaluating CSD effectiveness for coral reef restoration across wide areas and over many years is limited by the lack of robust and efficient monitoring approaches. Large-area reef imagery offers an attractive potential solution, but manual detection of CSDs within imagery is slow and limits the scalability of CSD monitoring. 2. We investigated whether machine learning classifiers could accurately detect CSDs in reef orthoimages and tested the performance of classifiers created following minimal manual annotation effort. Using freely available software, we first evaluated classifier performance in a single-site experiment using an orthoimage containing 989 CSDs deployed in Palau. We then also evaluated performance in a multi-site experiment using orthoimages containing a different CSD design deployed across seven sites in the central Great Barrier Reef, Australia. 3. In Experiment 1, classifiers trained on just 30 CSDs annotated within 10 minutes achieved mean recall and precision of 96.7% and 97.1% respectively, reducing manual annotation time by 95.6% whilst still detecting 98.8% of the number of devices found manually. Larger training sets yielded less reliable classifiers and required more manual effort. In Experiment 2, classifiers trained on 30 CSD annotations from one site performed excellently across seven orthoimages from multiple reefs, achieving mean recall and precision of 99.2% and 93.3%. 4. We present evidence that CSD classifiers can be highly effective across both single- and multi-site CSD deployments. In using a free and user-friendly software, we also demonstrate their accessibility to reef restoration practitioners. To facilitate wider uptake of CSD monitoring, we provide a step-by-step protocol for implementing CSD classifiers. By improving access to efficient, direct assessment of intervention outcomes, this method can play a vital role in guiding the enhancement of approaches aiming to restore coral reefs.

ecology↗

bioRxiv2024

Analysing drivers of worldwide tidal wetland change

Tidal wetlands are dynamic coastal ecosystems that can change in extent in response to a broad range of change drivers. We use high spatial resolution satellite imagery to estimate the relative influence of 18 classes of change drivers on observed tidal wetland gains and losses from 1999 to 2019, differentiating direct drivers as those observable at the site of ecosystem change, and indirect drivers as broader processes that influence changes without being directly visible. We developed a random sample of 2823 change detections from a global dataset of tidal wetland change and allocated each change event to driver classes using high-resolution time-series imagery. We identified that indirect drivers were the most widespread type of driver of tidal wetland change (70.9%), with flooding being the predominant driver for losses (47.5%) and unknown natural processes of change for gains (62.7%). Drivers often associated with climate change were evident in interpretations of wetland drivers, with increases in flooded area and reductions in vegetation cover suggesting the effects of relative sea level rise on tidal wetlands are observable in many areas. Our temporal analysis revealed that over 20 years, indirect drivers consistently contributed to larger proportions of gains and losses compared to direct drivers. Asia was the only continent where direct drivers of loss, such as agriculture (22.9%) and aquaculture (17.1%), outweighed indirect drivers, providing further evidence of the widespread transformation of Asias natural coastal ecosystems to anthropogenic shorelines. Globally, coastal land reclamations were mostly observed in mangrove ecosystems, where more than half of the observed losses were of anthropogenic origin. The most observed direct drivers of gains were altered land management and restoration, but none of them contributed to more than 5% of the total gains over 20 years. Our findings suggest a need for efficient conservation measures that allow the dynamic processes that characterise coastal ecosystems to persist, while simultaneously reducing the worldwide impact of direct human activities.

ecology↗