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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

publications

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Published in , 2021

Recommended citation: Gaggion, N., Ariel, F., Daric, V., Lambert, É., Legendre, S., Roulé, T., ... & Ferrante, E. (2021). ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture. GigaScience, 10(7), giab052. https://academic.oup.com/gigascience/article/10/7/giab052/6324285

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Published in , 2021

Recommended citation: Gaggion, N., Mansilla, L., Milone, D. H., & Ferrante, E. (2021, September). Hybrid graph convolutional neural networks for landmark-based anatomical segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 600-610). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-87193-2_57

Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

Published in , 2022

Recommended citation: N. Gaggion, L. Mansilla, C. Mosquera, D. H. Milone and E. Ferrante, "Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis," in IEEE Transactions on Medical Imaging, 2022, doi: 10.1109/TMI.2022.3224660. https://ieeexplore.ieee.org/document/9963582

Multi-center anatomical segmentation with heterogeneous labels via landmark-based models

Published in , 2023

Recommended citation: N. Gaggion, M. Vakalopoulou, D. H. Milone and E. Ferrante, "Multi-Center Anatomical Segmentation with Heterogeneous Labels Via Landmark-Based Models," 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230691. https://arxiv.org/abs/2211.07395

Unsupervised bias discovery in medical image segmentation

Published in , 2023

Recommended citation: Gaggion, N., Echeveste, R., Mansilla, L., Milone, D. H., & Ferrante, E. (2023, October). Unsupervised bias discovery in medical image segmentation. In Workshop on Clinical Image-Based Procedures (pp. 266-275). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-45249-9_26

CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images

Published in , 2024

Recommended citation: Gaggion, N., Mosquera, C., Mansilla, L., Saidman, J. M., Aineseder, M., Milone, D. H., & Ferrante, E. (2024). CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images. Scientific Data, 11(1), 511. https://doi.org/10.1038/s41597-024-03358-1

Fitting Skeletal Models via Graph-based Learning

Published in , 2024

Recommended citation: N. Gaggion, E. Ferrante, B. Paniagua and J. Vicory, "Fitting Skeletal Models via Graph-Based Learning," 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-4, doi: 10.1109/ISBI56570.2024.10635871. https://arxiv.org/abs/2409.05311

ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping

Published in , 2025

Recommended citation: Gaggion, N., Bonazzola, R., Legascue, M. F., Mammarella, M. F., Sol Rodriguez, F., Aballay, F. E., Catulo, F. B., Barrios, A., Accavallo, F., Villarreal, S. N., Ricardi, M. C., Petrillo, E., Blein, T., Ariel, F., & Ferrante, E. (2025). ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping. arXiv preprint arXiv:2504.14736. https://arxiv.org/abs/2504.14736

Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

Published in , 2025

Recommended citation: Gaggion, N., Matheson, B. A., Xia, Y., Bonazzola, R., Ravikumar, N., Taylor, Z. A., Milone, D. H., Frangi, A. F., & Ferrante, E. (2025). Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI. Medical Image Analysis, 103630. https://www.sciencedirect.com/science/article/pii/S136184152500177X

talks

High-throughput phenotyping of plan roots in temporal series of images using deep learning

Published: September 16, 2019

High-throughput phenotyping of plan roots in temporal series of images using deep learning

Published: November 11, 2019

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Published: September 27, 2021

Translating images into graphs: Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Published: October 11, 2021

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Published: November 10, 2021

Redes neuronales en grafo para segmentación de imágenes médicas

Published: December 06, 2021

ChronoRoot: A low-cost system for high-throughput phenotyping of plant root system architecture based on off-the-shelf electronics, 3D-printed hardware components, and deep learning models

Published: April 27, 2022

Nicolas Gaggion

Sitemap

publications

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

Multi-center anatomical segmentation with heterogeneous labels via landmark-based models

Unsupervised bias discovery in medical image segmentation

CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images

Fitting Skeletal Models via Graph-based Learning

ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping

Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

talks

High-throughput phenotyping of plan roots in temporal series of images using deep learning

High-throughput phenotyping of plan roots in temporal series of images using deep learning

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

Translating images into graphs: Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

Redes neuronales en grafo para segmentación de imágenes médicas

ChronoRoot: A low-cost system for high-throughput phenotyping of plant root system architecture based on off-the-shelf electronics, 3D-printed hardware components, and deep learning models

teaching

Student teaching assistant - Digital Image Processing

Teaching assistant - Deep Learning

Teaching assistant - Informatics

Teaching assistant - Linear Algebra, Optimization and Machine Learning

Teaching assistant - Data Science I

Teaching assistant - Deep Learning

Teaching assistant - Data Science II

Part time professor - Computing Systems

Adjunct Professor - Numerical Methods

Adjunct Professor - Algorithm Design