887 citations
- Centre National de la Recherche ScientifiqueFR81 papers
- Sorbonne UniversitéFR79 papers
- InsermFR75 papers
- Pitié-Salpêtrière HospitalFR52 papers
- Assistance Publique – Hôpitaux de ParisFR43 papers
- Institut national de recherche en sciences et technologies du numériqueFR25 papers
- Institut des Sciences MoléculairesFR24 papers
- ARAMIS: Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain sain et pathologiqueFR17 papers
- Université Paris CitéFR11 papers
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- Allen Institute for Brain ScienceUS8 papers
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15 papers · 1 filter
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
Tiago Assis, Colin P. Galvin, Joshua P. Castillo +12
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative…
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Tiago Assis, Ines P. Machado, Benjamin Zwick +2
Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness…
Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
Lisa Hemforth, Baptiste Couvy-Duchesne, Kevin De Matos +34
Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population.…
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation
Sophie Loizillon, Simona Bottani, Stéphane Mabille +6
The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Ravi Hassanaly, Camille Brianceau, Maëlys Solal +2
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel…