23 citations · 44 across the 16 of their papers we have counts for
18 papers
OREHAS: A fully automated deep-learning pipeline for volumetric endolymphatic hydrops quantification in MRI
Caterina Fuster-Barceló, Claudia Castrillón, Laura Rodrigo-Muñoz +4
We present OREHAS (Optimized Recognition & Evaluation of volumetric Hydrops in the Auditory System), the first fully automatic pipeline for volumetric quantification of endolymphat…
Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images
Roman Kinakh, Gonzalo R. Ríos-Muñoz, Arrate Muñoz-Barrutia
Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a B…
The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course
Gonzalo R. Ríos-Muñoz, Caterina Fuster-Barcelo, Arrate Muñoz-Barrutia
This study examines the integration of digital tools in project-based learning within a Biomedical Engineering course to enhance collaboration, transparency, and assessment fairnes…
Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology
Caterina Fuster-Barcelo, Gonzalo R. Rios-Munoz, Arrate Munoz-Barrutia
This study examines the integration of digital collaborative tools and structured peer evaluation in the Machine Learning for Health master's program, through the redesign of a Bio…
SAMJ: Fast Image Annotation on ImageJ/Fiji via Segment Anything Model
Carlos Garcia-Lopez-de-Haro, Caterina Fuster-Barcelo, Curtis T. Rueden +9
Mask annotation remains a significant bottleneck in AI-driven biomedical image analysis due to its labor-intensive nature. To address this challenge, we introduce SAMJ, a user-frie…
Alzheimer's disease detection in PSG signals
Lorena Gallego-Viñarás, Juan Miguel Mira-Tomás, Anna Michela-Gaeta +4
Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-s…
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur +24
Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but a…
BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging
Wanlu Lei, Caterina Fuster-Barceló, Gabriel Reder +2
We present the BioImageIO Chatbot, an AI assistant powered by Large Language Models and supported by a community-driven knowledge base and toolset. This chatbot is designed to c…
JDLL: A library to run Deep Learning models on Java bioimage informatics platforms
Carlos Garcia Lopez de Haro, Stephane Dallongeville, Thomas Musset +6
We present JDLL, an agile Java library that offers a comprehensive toolset/API to unify the development of high-end applications of DL for bioimage analysis and to streamline their…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation
Laura Nicolás-Sáenz, Agapito Ledezma, Javier Pascau +1
In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of metho…
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
Selin Aviyente, Alejandro Frangi, Erik Meijering +6
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical i…
Translational Lung Imaging Analysis Through Disentangled Representations
Pedro M. Gordaliza, Juan José Vaquero, Arrate Muñoz-Barrutia
The development of new treatments often requires clinical trials with translational animal models using (pre)-clinical imaging to characterize inter-species pathological processes.…
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…
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek +3
Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells emb…
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…
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations
Pedro M. Gordaliza, Juan José Vaquero, Sally Sharpe +2
We propose a learning method well-suited to infer the presence of Tuberculosis (TB) manifestations on Computer Tomography (CT) scans mimicking the radiologist reports. Latent featu…
Fast space-variant elliptical filtering using box splines
Kunal Narayan Chaudhury, Arrate Munoz-Barrutia, Michael Unser
The efficient realization of linear space-variant (non-convolution) filters is a challenging computational problem in image processing. In this paper, we demonstrate that it is pos…