Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.

Epilepsy is a common neurological disease characterized by spontaneous recurrent seizures. Resection of the epileptogenic tissue may be needed in approximately 25% of all cases due to ineffective treatment with anti-epileptic drugs. The surgical intervention depends on the correct detection of epileptogenic zones. The detection relies on invasive diagnostic techniques such as Stereotactic Electroencephalography (SEEG), which uses multi-modal fusion to aid localizing electrodes, using pre-surgical magnetic resonance and intra-surgical computer tomography as the input images. Moreover, it is essential to know how to measure the performance of fusion methods in the presence of external objects, such as electrodes. In this paper, a literature review is presented, applying the methodology proposed by Kitchenham to determine the main techniques of multi-modal brain image fusion, the most relevant performance metrics, and the main fusion tools. The search was conducted using the databases and search engines of Scopus, IEEE, PubMed, Springer, and Google Scholar, resulting in 15 primary source articles. The literature review found that rigid registration was the most used technique when electrode localization in SEEG is required, which was the proposed method in nine of the found articles. However, there is a lack of standard validation metrics, which makes the performance measurement difficult when external objects are presented, caused primarily by the absence of a gold-standard dataset for comparison.

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method’s efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used.

Introducción: en pacientes con epilepsia del lóbulo temporal refractarios que no son candidatos a cirugía, se debe considerar la estimulación eléctrica cerebral como una opción. Contenido: la estimulación eléctrica cerebral es la administración directa de pulsos eléctricos al tejido nervioso que permite modular un sustrato patológico, interrumpir la manifestación clínica de las crisis y reducir la gravedad de estas. Así, dada la importancia de estos tratamientos para los pacientes con epilepsia del lóbulo temporal refractaria, se hace una revisión de cuatro tipos de estimulación eléctrica. La primera, la del nervio vago, es una buena opción en crisis focales y crisis generalizadas o multifocales. La segunda, la del hipocampo, es más útil en pacientes no candidatos a lobectomía por riesgo de pérdida de memoria, con resonancia magnética normal o sin esclerosis mesial temporal. La tercera, la del núcleo anterior, es pertinente principalmente en pacientes con crisis focales, pero debe realizarse con precaución en pacientes con alto riesgo de cambios cognitivos, como los ancianos, o en los que presentan alteración del estado de ánimo basal, y, por último, la del núcleo centromediano se recomienda para el tratamiento crisis focales en el síndrome de Rasmussen y crisis tónico-clónicas en el síndrome de Lennox-Gastaut. Conclusiones: el interés por la estimulación eléctrica cerebral ha venido aumentando, al igual que las estructuras diana en las cuales se puede aplicar, debido a que es un tratamiento seguro y eficaz en pacientes con epilepsia del lóbulo temporal para controlar las crisis, pues disminuye la morbimortalidad y aumenta la calidad de vida.