collaborators

9 papers

eess.IV2026

Unsupervised Adversarial Domain Adaptation for Uterine layer Segmentation: From Labeled Cine to Unlabeled Dynamic EPI MRI

Smiti Tripathy, Milauni Desai, Jordina Aviles Verdera +1

Uterine peristalsis is a key physiological phenomenon responsible for various functions across the menstrual cycle, intimately linked to uterine wall microstructure. Alterations in…

cs.CV2026

Panda: Unsupervised Pelvic Anomaly Detection for Real-Time MR Imaging

Anika Knupfer, Maximilian Lindholz, Johanna Paula Müller +4

Female pelvic diseases remain an under researched area characterized by often delayed diagnosis. While pelvic MRI offers superior soft-tissue contrast for diagnosis and image-guide…

eess.IV2026

Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation

Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet +10

Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studying neurodevelopment, but remains challenging due to data heterogeneity and limited annota…

eess.IV2026

Female-RHINO: A Real-Time Scanner-Integrated Framework for Automated Quantitative Uterine MRI Analysis and Structured Reporting

Deepak Bhatia, Saad Ahmad, Smiti Tripathy +7

Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools. This wo…

cs.CV2026

Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging

Anika Knupfer, Johanna P. Müller, Jordina A. Verdera +9

Pelvic diseases in women of reproductive age represent a major global health burden, with diagnosis frequently delayed due to high anatomical variability, complicating MRI interpre…

eess.IV2026

Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg

Ziyao Shang, Misha Kaandorp, Kelly Payette +9

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis…