The 29th edition of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) took place in Strasbourg, France, from the 27th of September to the 1^st of  October 2026. The conference brings together international researchers, engineers and clinicians working at the intersection of artificial intelligence, medical imaging and biomedical engineering.

Several members of ViCOROB participated in this year’s edition, contributing to scientific presentations and activities within the international research community. Their participation covered different aspects of medical image computing, from automated lesion detection to research on medical image simulation and synthesis.

Automated skin lesion detection

Anup Saha presented the work entitled “Automated Skin Lesion Detection in Total Body Photography: A Multi-Architecture Benchmark” (Yamin, M. et al.) as a spotlight, at the ISIC workshop held in conjunction with MICCAI 2026.

The study is part of the European project iToBoS (Intelligent Total Body Scanner for Early Detection of Melanoma), coordinated by Prof. Rafael Garcia (ViCOROB). It addresses the automated detection of skin lesions in total body photography, an approach that involves analysing images covering the entire body to support the assessment of skin lesions. The research compares different model architectures to evaluate their performance in this task.

Contributions to the international research community

ViCOROB’s participation also involved scientific activities beyond the presentation of research findings, including contributions to conference sessions and initiatives within the MICCAI community.

Mariano Cabezas Grebol served as Session Chair for the 05A oral session titled Image Segmentation III from the main conference. The session focused on different segmentation approaches, including contributions based on foundation models. Together with Neslihan Bayramoglu, they introduced all 5 speakers and mediated during the Q&A after the presentation. He was also part of the organizing committee for the Ischemic Stroke Lesion Segmentation Challenge (ISLES’26), which focuses on the segmentation of lesions associated with ischaemic stroke and was held on the first day of the satellite events. During the satellite events, he also volunteered with the RISE ambassador program to present two posters for authors that could not attend the conference.

Adrià Casamitjana Díaz was a member of the organising committee for the 11th edition of the Simulation and Synthesis in Medical Imaging (SASHIMI) workshop, which focuses on research into the simulation and synthesis of medical images. A total of 36 out of 57 outstanding contributions were presented during the workshop, with focus on advances in image-to-image translation and image synthesis as well as discussion on the downstream applications of generative models.

Scientific posters and ongoing research

Poster sessions provided an additional opportunity to present research and exchange ideas with the international scientific community. Hasna R. Hamadache, Agustin Cartaya Lathulerie and Hadeel Awwad presented posters showcasing their ongoing research.

– A. Cartaya et al., Latent Conditional Rectified Flow for MRI Synthesis Across Modalities and Field Strengths in the MRIxFields2026 Challenge.
This work explores the consistent translation across scanners of different quality, improving the generalizability of downstream tasks such as image segmentation across hospitals.

– R.E. Hamadache et al., nnU-Net and Domain Randomization for Automated Segmentation of Moderate-to-Severe Traumatic Brain Injury.
This work studies the use of synthetic data to complement real cases in small sample size studies, improving the performance in small lesion detection.

Advancing research in medical image computing

The presence and active participation of ViCOROB members at MICCAI 2026, the leading international conference on medical image computing, reflects the quality of research conducted in our group. It has provided a unique opportunity to network, exchange ideas, and connect with frontier research labs in the field.

The current focus of the community is on learning robust and generalizable representations that deliver practical clinical value. This aligns closely with our work at ViCOROB: building generalizable normative models, leveraging domain randomization and image synthesis to train robust segmentation and registration architectures, and integrating longitudinal data to enrich clinical findings. As the MICCAI community gradually shifts toward world models, ViCOROB is well positioned to lead and contribute to this evolving paradigm.

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By: Ricardo Montoya del Angel
Supervised by:  Dr. Robert Martí Marly

Thesis Overview

Artificial intelligence (AI) holds high potential for automated medical diagnosis, but relies heavily on vast, well-annotated datasets. In specialized fields like Contrast-Enhanced Mammography (CEM) or point-of-care echocardiography, data scarcity remains a critical bottleneck for clinical deployment. This thesis investigates probabilistic generative modeling strategies to synthesize realistic, clinically valid medical data across three levels of complexity:

1. Physically-Informed Uncertainty: Maps acquisition and annotation uncertainties into bounded generative variance, enhancing radiomic biomarker prediction in small CEM cohorts.
2. Controllable Mammography Synthesis: Leverages latent diffusion models for full-field synthesis and localized lesion inpainting, transferring knowledge to data-scarce CEM.
3. Efficient Video Synthesis: Combines 1D tokenization and masked transformers to reconstruct missing echocardiographic views (2-chamber from 4-chamber), enabling Ejection Fraction estimation on portable ultrasound devices.

The findings support the idea that synthetic data generated with physical and clinical rigor effectively overcomes clinical data constraints, enhancing diagnostic accuracy and decision support in clinical practice.

Photo: Illustration of the research developed in the doctoral thesis on generative modeling for medical data synthesis.

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The training was carried out through a collaboration between the UdiGitalEdu and GREPAI research groups at the University of Girona and was led by Karo Kunde, lecturer of the course English: Competency-Based Learning.

The participants were students from three different Education degree programmes enrolled in this third-year course. As future teachers, they represent a particularly relevant group for the project: AI is already transforming how languages can be learned and taught, and teacher education has an important role to play in helping new educators approach these technologies critically, ethically and with a clear pedagogical purpose.

As part of the initiative, the students worked through Modules 1 and 2 of the AI2ImproveFLL training course. Through the activities, they explored the possibilities and limitations of Artificial Intelligence in foreign language education and considered how AI can support teachers’ work while remaining grounded in sound pedagogical principles.

Bringing the first two modules of the course into initial teacher education at UdG provided an opportunity to engage a large group of future educators in these questions at an early stage of their professional development. Beyond learning about particular AI tools, students were encouraged to reflect on the changing role of teachers and learners, the pedagogical choices behind the use of AI, and the need to keep human judgement and educational purposes at the centre.

The experience also contributes directly to one of the central objectives of AI2ImproveFLL: strengthening the AI literacy of pre-service and in-service language teachers and teacher educators, while developing a professional-development model that can be used and adapted across different European educational contexts.

For the University of Girona, the training represents another step in connecting research, teacher education and classroom practice, while giving future teachers the opportunity to critically explore technologies that are likely to play an increasingly important role throughout their professional careers.

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Observing the Ocean Over Time

Marine ecosystems change both over time and across space. Understanding these changes requires observations that go beyond one-off studies, particularly when examining seabed environments.

PLOME has explored how fixed and mobile autonomous systems can work together to support long-duration marine observation. The concept combines benthic stations, which remain on the seabed, with autonomous underwater vehicles (AUVs) capable of moving through the environment and collecting data.

The stations provide observations from fixed locations, while AUVs add mobility and allow the observation area to be expanded. The project has also developed and tested underwater communication and docking technologies to facilitate interaction between the different elements and support longer-duration autonomous missions.

Autonomous Observation Trials at Sea

Several of these technologies were tested under real marine conditions during PLOME’s experimental campaigns.

In 2024, the Girona 1000 AUV, developed at the University of Girona, demonstrated autonomous docking and undocking with an underwater docking station. The vehicle was able to start a mission from the station, carry it out and autonomously return to the station.

During the final sea trials in July 2025, the consortium tested different elements of the PLOME concept aboard the oceanographic research vessel Sarmiento de Gamboa, including AUVs, benthic landers and underwater communication systems.

During these trials, the Girona 1000 also carried out adaptive surveys: images were analyzed on board and, when potentially relevant features were detected, the vehicle was able to modify its trajectory. This approach allows an autonomous vehicle to adapt its mission based on information gathered during the survey.

Why Are These Advances Important?

Combining fixed and mobile autonomous systems makes it possible to collect observations at different spatial and temporal scales, which is relevant for the study of marine ecosystems.

PLOME has explored how these technologies can support long-duration and adaptive observation of marine environments, generating information that can contribute to research and ecosystem monitoring, as well as to the development of ecological indicators for the assessment of Marine Protected Areas.

PLOME on Video

Project Summary

Project: PLOME – Long-Term Platform for the Observation of Marine Ecosystems
Reference: PLEC2021-007525
Duration: December 2021 – September 2025
Coordinator: University of Girona
Partners: Universitat Politècnica de Catalunya, Universitat de les Illes Balears, Universidad Politécnica de Madrid, Institute of Marine Sciences (ICM-CSIC) and IQUA Robotics.

Discover the PLOME project and its scientific results →

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Thesis Overview

Brain pathology analysis through medical imaging is critical for clinical diagnosis, treatment planning, and patient outcome prediction. Computed tomography (CT) and magnetic resonance imaging (MRI) have become essential imaging modalities for assessing two major classes of brain pathologies: intracranial hemorrhage (ICH) and brain tumors. However, manual interpretation and analysis of these imaging studies are time-consuming, labor-intensive, and susceptible to intra- and interobserver variability. In recent years, artificial intelligence-based methods have shown great potential in automating the detection, segmentation, and quantification of brain lesions, thereby supporting clinical workflows and improving diagnostic accuracy. Nevertheless, these methods typically rely on large amounts of annotated data to achieve high performance. In medical imaging, obtaining such annotations is particularly challenging due to the need for expert knowledge, as well as the significant time and economic costs associated with manual labeling. As a result, while vast amounts of medical imaging data are routinely acquired in clinical practice, only a small fraction is accompanied by high-quality annotations. This imbalance highlights the need for approaches that can effectively leverage limited labeled data, motivating the development of learning strategies under limited supervision.

The main goal of this PhD thesis is to develop robust deep learning methodologies for neuroimaging analysis under limited supervision. To address annotation scarcity, we explore several complementary strategies. In the first stage, we propose a framework for hematoma expansion (HE) prediction in spontaneous intracerebral hemorrhage (ICH) using baseline CT scans. This approach combines a classification model with synthetic CT image generation to enhance robustness and alleviate data limitations. Our results show that integrating synthetic data with conventional augmentation improves both predictive performance and generalization. In the second stage, we develop semi-supervised deep learning frameworks that leverage both labeled and unlabeled data for binary brain lesion segmentation in brain tumors and spontaneous ICHs. We introduce a novel method that combines feature perturbation with mutual learning, achieving significant improvements in low-annotation settings (10–20% labeled data). In the third stage, we extend semi-supervised learning to multi-class hemorrhage segmentation, a more complex task requiring the differentiation of multiple hemorrhage subtypes. We conduct a comprehensive benchmark of established semi-supervised methods within a unified nnUNet framework, providing a systematic evaluation of different strategies for this setting.

All proposed methodologies are evaluated on both institutional datasets from collaborating hospitals and public international benchmarks, enabling objective comparison with state-of-the-art approaches. Notably, this work was carried out in close collaboration with the medical team at Hospital Dr. Josep Trueta, with clinical expertise integrated throughout the research process from problem formulation and data acquisition to result interpretation and validation. By combining deep learning techniques with clinical insight, this thesis aims to deliver practical tools that support clinical decision-making and ultimately improve patient care in brain tumors and ICHs. Emphasis is placed on reproducibility and clinical applicability, with all methods and experimental pipelines made publicly available to facilitate future research and the development of reliable automated decision-support systems.

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The delegation from the Medical Imaging Lab included Dr. Robert Martí, who served as a member of the workshop committee and chaired a scientific session, alongside PhD candidates Hadeel Awwad and Enric Sena. The workshop provided a valuable platform for the team to engage with international imaging peers, including newly onboarding researchers like Enric.

The work presented falls under the framework of the IMPACT project, which leverages deep learning to drive innovation in image analysis, optimize lesion detection, and advance treatment response prediction to promote personalized healthcare outcomes.

PhD candidate Hadeel Awwad presented a poster detailing preliminary results on predicting pathologic complete response (pCR) from Breast MRI using the multi-centre MAMA-MIA dataset. To find the configuration with the highest discriminative power, the study conducted a systematic evaluation of 78 different radiomics configurations spanning spatial regions—specifically comparing the primary tumour and a peritumoural ring—as well as temporal representations and mathematical transforms.

The preliminary findings revealed that simple, dynamic, first-order peritumoural signals consistently achieved the highest classification accuracy, outperforming static or purely intratumoural features. These results justify integrating these localized temporal variables into future comprehensive models to improve treatment response prediction.

Beyond the scientific presentations, the workshop served as an excellent venue for strengthening ties within the international breast imaging community. The team took advantage of the event to establish new collaborations and lay the groundwork for future joint research initiatives with European and international partners.

This work has been funded by the IMPACT project from the Ministerio de Ciencia e Innovación y Universidades of Spain, and the PhD grant IFUdG2024 from the University of Girona.

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Thesis Overview

This PhD thesis focuses on the development of artificial intelligence methods based on deep learning for the analysis of brain images and, ultimately, to help improve the diagnosis and treatment of common neurological diseases such as stroke and meningioma.

Stroke is one of the leading causes of death and disability worldwide. It can be either ischemic or hemorrhagic, and in both cases the diagnosis relies on brain imaging examinations, most commonly computed tomography (CT). Meningioma, on the other hand, is the most common primary brain tumor. It is often detected and monitored through periodic magnetic resonance imaging (MRI) examinations, and when treatment is required, especially radiotherapy, treatment planning is based on these images to accurately define the area to be irradiated. Therefore, medical imaging is a key tool throughout all stages of the clinical management of these diseases.

In this context, this thesis explores how deep learning can make different steps of the clinical workflow more efficient and accurate. First, a method is proposed to predict the possible evolution of intracerebral hemorrhage in patients with hemorrhagic stroke using only the initial available CT scan. One of the main challenges is the limited availability of clinical data, and therefore a strategy is also proposed to generate synthetic examples simulating possible lesion evolutions. These synthetic images are used to train the model and improve its predictive capability, achieving competitive results compared with state-of-the-art methods.

Second, a system is developed to detect large vessel occlusions, one of the main causes of ischemic stroke, from CT angiography images, and to identify the affected vascular segment. The method focuses on a specific anatomical region, the Circle of Willis, which contains the most relevant vessels. This allows the analysis to be simplified, the process to be accelerated, and a high level of reliability to be maintained. The results have been validated using real hospital data and independent test sets, demonstrating good generalization capability.

Third, a method is presented for the segmentation of meningioma tumors in MRI images used for radiotherapy planning. The main contribution is a richer representation of the tumor, which considers not only the lesion itself but also its transition zone. This additional information improves segmentation accuracy and achieves better results, even in comparison with other recent approaches. The method was evaluated and validated in an international challenge, where it achieved first place.

Overall, this thesis demonstrates how artificial intelligence can contribute to making brain image analysis more automated, accurate, and efficient. An important part of the work was carried out in collaboration with the medical team at Hospital Dr. Josep Trueta, integrating real clinical knowledge throughout all stages of the project. The ultimate goal is to provide tools that can have a direct impact on clinical practice and contribute to improving the care of patients with stroke and brain tumors.

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The project aims to develop new machine learning approaches for the Girona1000 AUV, that integrates side-scan sonar with a dual-camera optical system, to enhance underwater mapping and blue carbon inventory, providing valuable information for environmental monitoring and conservation efforts. By generating and analysing side-scan sonar maps in real time, the robot can interpret the underwater environment as it explores the seafloor and autonomously adapt its mission based on the information it acquires, improving the efficiency and quality of underwater inspection and mapping operations.

The work was presented by Dr. Nuno Gracias, together with PhD candidates Hayat Rajani and Valerio Franchi from CIRS. The poster introduce the first underwater stereo event-camera setup developed within the project. The system uses an AMD Kria KV260 to preprocess the large volume of event data before feeding it to a SpiNNode neuromorphic board, built around the SpiNNaker2 chip, which runs spiking neural networks for classification. The high temporal resolution and high dynamic range of event cameras cope with the rapid motion and uneven illumination underwater, while their low power consumption supports the long survey missions that broad blue carbon inventory requires.

Valerio and Hayat also took the opportunity to present ongoing work from two other projects. One of the posters introduced a multi-robot system for total body photography at dermatoscopic image quality, a line of research begun under the iToBoS project and continued since its conclusion. The work-in-progress framework uses four UR10 manipulators with end-effector cameras, combining view planning and multi-robot scheduling with a real-time mole tracker in a visual servoing loop, allowing it to adapt to individual patient anatomies and capture consistent high-resolution images while reducing examination time and operator dependency. The other poster reported preliminary results from the ongoing tech transfer project OculaRobot an autonomous framework for precision intravitreal injection, one of the highest-volume medical procedures worldwide. The system pairs a 6-DOF collaborative manipulator with a trifocal stereoscopic vision rig for real-time 3D tracking of the eye and needle, using visual servoing and force feedback to target sub-millimetre positioning accuracy. Both posters were presented at the well-regarded workshop on robot-assisted medical imaging, RAMI .

Additionally, Hayat presented a poster from the work carried out at Heriot-Watt University, introducing a controlled benchmark designed to systematically evaluate modern 3D reconstruction methods under variations in medium and lighting. The benchmark comprises 13 datasets spanning two media (air and water) and three lighting conditions (ambient, artificial, and mixed), with additional variations in motion type, scanning pattern, and initialization trajectory, resulting in a diverse set of sequences.

Funded by the Agencia Estatal de Investigación (AEI) and the NextGenerationEU programme, the IURBI project is now entering its final stage. The technologies developed within the project contribute to the advancement of more intelligent and autonomous underwater robotic systems, with potential applications in marine exploration, environmental monitoring and the conservation of marine ecosystems, reinforcing the role of technological innovation in addressing scientific and societal challenges.

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As part of the conference programme, the presentation entitled “DIVE INTO LEARNING Project: Inclusive STEAM Education with Low-Cost Underwater Robots” was delivered to showcase this Erasmus+ project, which aims to promote more inclusive STEAM education through the use of low-cost underwater robots and project-based learning methodologies.

The project seeks to foster social inclusion, support teachers, and enhance the participation of students from underrepresented and minority groups through innovative educational activities. Within this framework, DIVE Into Learning is developing a curriculum based on underwater robotics and marine sciences to bring technology and research into new educational contexts while ensuring that all learners can successfully acquire knowledge and skills in these disciplines.

Participation in CUIEET, a conference that brings together engineering schools from across Spain, provided an opportunity to increase the project’s visibility at the national level and to share this educational approach with the academic community interested in educational innovation and inclusive STEAM methodologies.

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La sessió va apropar al públic alguns dels avenços més recents en l’estudi del cervell humà i el paper que hi juga actualment la intel·ligència artificial. Durant la conferència, Casamitjana va explicar com les noves eines computacionals permeten connectar diferents nivells d’estudi del cervell, des de l’escala microscòpica de les cèl·lules fins a l’anàlisi macroscòpica dels sistemes cerebrals i la seva interrelació.

La conferència també va presentar el projecte NextBrain, una iniciativa de recerca que utilitza tècniques d’intel·ligència artificial per oferir un mapa complet de l’arquitectura cel·lular del cervell sense precedents. Les tècniques desenvolupades permeten una anàlisi ràpida, precisa i accessible d’imatges cerebrals en individus vius. Aquest enfoc permet identificar patrons cerebrals complexos, facilitant l’estudi del desenvolupament i l’envelliment cerebral, així com en l’impacte de diverses malalties neurodegeneratives. Totes les eines del projecte NextBrain han estat publicades lliurement a la comunitat científica per a futurs estudis: https://github-pages.ucl.ac.uk/NextBrain

Veiem, doncs, com la intel·ligència artificial esdevé una tecnologia crucial en l’estudi del cervell, ja que permet examinar grans quantitats de dades amb patrons complexes. Obre les portes a estudis fins ara inimaginables, integrant dades de múltiples fonts que anticipin la detecció de malalties cerebrals. Ens permetrà, per exemple, poder dissenyar protocols i intervencions personalitzades i, fins i tot, crear bessons digitals que permetin simular la evolució dels pacients i l’impacte dels tractaments.

El cicle “Últimes fronteres. La ciència al segle XXI”, organitzat per la Casa de Cultura de Girona, té com a objectiu apropar la recerca científica i tecnològica més actual a la ciutadania. La sessió es va gravar en vídeo i estarà disponible al canal de Youtube de la Casa de Cultura de Girona.

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