PIPPI 2025 Schedule

South Korea local time
27th September 2025, Daejeon Convention Centre

13:30 - 13:40 Welcome & Introduction
13:40 - 14:30 PIPPI Keynote Dr. Kiho Im, Associate Professor of Pediatrics at Harvard Medical School
14:30 - 15:00 Introduction to Poster session and 2min Power Pitch Poster Presentations

P1: Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction, Thomas Sanchez (University of Lausanne (UNIL)) et al.
P2: D-SVG: Diffusion Based Slice-to-Volume Generation with Implicit Neural Representation for Fetal Brain MRI, Yao Lv (South China University of Technology) et al.
P3: Anatomically-Informed Dynamic Weighting for Robust Semi-Supervised Fetal MRI Segmentation, Aaron Oleander (Hebrew University of Jerusalem) et al.
P4: Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization, Mariina Grifell I Plana (Universitat Politècnia de Catalunya) et al.
P5: Continuous Spatio-Temporal Representation with Implicit Neural Networks for Fetal Brain MRI Atlas Construction, Kai Zhang (Shanghaitech University) et al.
P6: FetGEs: a Deep Learning Approach for Fetal MRI Ganglionic Eminence Segmentation, Tommaso Ciceri (IRCCS Eugenio Medea) et al.
P7: Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation, Johannes Tischer (Medical University of Vienna) et al.
P8: Enhancing Fetal Brain MRI Segmentation in Ventriculomegaly Using Generative AI-Augmented Pathological Data, Misha Kaandorp (University Children’s Hospital Zurich) et al.
P9: Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping, Dondu-Busra Bulut (University of Lausanne) et al.
P10: Quantifying Fetal Periventricular White Matter Development using Multimodal MRI, Helena Sousa (King’s College London) et al.
P11: Automated biometry for assessing cephalopelvic disproportion in 3D 0.55T fetal MRI at term, Alena Uus (King’s College London) et al.
P12: Robustness and Diagnostic Utility of Super-Resolution Fetal Brain MRI, Ema Masterl (University of Ljubljana) et al.

15:00 - 16:00 Break & Poster Session
16:00 - 17:00 Oral Session (8min presentation + 2min Q&A each)

16:00 - NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg, Nashira Baena (King’s College London) et al.
16:10 - Robust Alignment of the Human Embryo in 3D Ultrasound using PCA and an Ensemble of Heuristic, Atlas-based and Learning-based Classifiers, Nikolai Herrmann (Erasmus MC) et al.
16:20 - FetalExtract-LLM: Structured Information Extraction from Free-Text Fetal MRI Reports Based on Privacy-Ensuring Open-weights Large Language Models, Mingxuan Liu (Tsinghua University) et al.
16:30 - Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI, Xinliu Zhong (Emory University) et al.
16:40 - PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk, Diego Fajardo-Rojas (King’s College London) et al.
16:50 - Towards a comprehensive morphological, dynamic and functional investigation of the pediatric bowel on 0.55T, Michael Kitzberger (Uniklinikum Erlangen) et al.

17:00 - 17:50 PIPPI Circle Debate - Should AI be used in early-life imaging?
17:50 - 18:00 PIPPI Best Paper Award & Close

PIPPI 2025 Keynote Talk
Dr. Kiho Im, Associate Professor of Pediatrics at Harvard Medical School
Toward early neurodevelopmental biomarkers: Comprehensive fetal brain MRI analysis

Dr. Kiho Im is an Associate Professor of Pediatrics at Harvard Medical School, and a Staff Scientist in the Division of Newborn Medicine, FNNDSC at Boston Children’s Hospital. Dr. Im received his PhD in Biomedical Engineering from Hanyang University, South Korea in 2009. He has expertise in quantitative neuroimage analysis using structural and diffusion magnetic resonance imaging (MRI) data. His research goal is to provide unique and biologically relevant imaging biomarkers that not only help us to better understand normal and abnormal brain development, but also aid in the detection and diagnosis of disease. In particular, his team focuses on quantitative analysis of sulcal pits and patterns; gyral based structural brain connectivity/network analysis; genetic and environmental effects on brain development; and advanced fetal brain MRI processing and analysis using deep learning.

PIPPI Circle Panel Debate
Should AI be used in early-life imaging?