Publications (12)
Learning Gaze-aware Compositional GAN
Nerea Aranjuelo, Siyu Huang, Ignacio Arganda-Carreras +4
Gaze-annotated facial data is crucial for training deep neural networks (DNNs) for gaze estimation. However, obtaining these data is labor-intensive and requires specialized equipm…
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
Zudi Lin, Donglai Wei, Mariela D. Petkova +12
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. Howe…
3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction
Guus Engels, Nerea Aranjuelo, Ignacio Arganda-Carreras +2
This paper presents a new approach to 3D object detection that leverages the properties of the data obtained by a LiDAR sensor. State-of-the-art detectors use neural network archit…
Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
Karim Hammoudi, Halim Benhabiles, Mahmoud Melkemi +4
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the wo…
Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners
Xabier Lekunberri, Ahmad Kamal, Izaro Goienetxea +5
Purse seiners play a crucial role in tuna fishing, as approximately 69% of the world's tropical tuna is caught using this gear. All tuna Regional Fisheries Management Organizations…
Group-wise 3D registration based templates to study the evolution of ant worker neuroanatomy
Ignacio Arganda-Carreras, Darcy G Gordon, Sara Arganda +2
The evolutionary success of ants and other social insects is considered to be intrinsically linked to division of labor and emergent collective intelligence. The role of the brains…
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Donglai Wei, Kisuk Lee, Hanyu Li +13
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due t…
Inferring spatial relations from textual descriptions of images
Aitzol Elu, Gorka Azkune, Oier Lopez de Lacalle +3
Generating an image from its textual description requires both a certain level of language understanding and common sense knowledge about the spatial relations of the physical enti…
Deep learning based domain adaptation for mitochondria segmentation on EM volumes
Daniel Franco-Barranco, Julio Pastor-Tronch, Aitor Gonzalez-Marfil +2
Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning…
A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction
Aitor Iglesias, Nerea Aranjuelo, Patricia Javierre +3
We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated…
Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes
Daniel Franco-Barranco, Arrate Muñoz-Barrutia, Ignacio Arganda-Carreras
Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a numb…
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Leander Lauenburg, Zudi Lin, Ruihan Zhang +6
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segme…