Lab News | LCPN

Pulkit is presenting at the MICCAI PIPPI 2026 workshop!

Sun, 27 Sep 2026 00:00:00 +0000

See here for more information about the workshop: https://pippiworkshop.github.io/index.html#header

Pulkit will be giving a talk at the 2026 Postmortem Research in Dementia Workshop!

Thu, 13 Aug 2026 00:00:00 +0000

See here for more information about the event: https://www.eventbrite.com/e/2026-postmortem-research-in-dementia-workshop-registration-1983955682854

LCPN will be at FIT'NG!

Fri, 10 Jul 2026 00:00:00 +0000

Join us at the FIT’NG 2026 Annual Conference!

Our group will be presenting the below:

“High-resolution multi-modal postmortem MRI of the human infant brain: Methodological developments for tissue segmentation, cortical reconstruction and anatomical parcellation” (poster)

“Robust infant brain MRI segmentation on clinical datasets” (poster + flash talk)

“Comparison of automated brain morphometry from low-field and high-field MRI acquisitions in South African neonates and 6-year-old children” (poster)

LCPN will be at OHBM!

Sun, 14 Jun 2026 00:00:00 +0000

The LCPN will have multiple posters at OHBM 2026 (Young et al, Huszar et al) and Lilla is participating in an educational course about “Open-science in Infant MRI”. Our abstracts can be found here: https://abstractatlas.brainkb.org/ohbm2026/

Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

Sat, 09 May 2026 00:00:00 +0000

Read the full preprint here: https://www.biorxiv.org/content/10.64898/2026.05.07.722301v1.full

Abstract

High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brain anatomy at spatial scales not achievable in vivo and provides a unique opportunity to link morphometric measurements with the underlying pathology. Despite these advantages, robust computational tools for automated anatomical segmentation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, as well as an evolving tissue contrast combined with fixation-induced signal alterations and variability in postmortem preparation make standard neuroimaging pipelines unusable for postmortem infant MRI. In this work, we introduce a one-of-its-kind multi-modal high-resolution postmortem infant MRI dataset and a unified computational framework that combines deep learning-based volumetric segmentation with surface-based cortical reconstruction and anatomical parcellation in native subject space resolution. To address the pronounced domain shift inherent to postmortem MRI, we develop a postmortem-specific synthetic data generation engine (PostSynth) that explicitly models fixation-driven postmortem imaging characteristics. In particular, we incorporate postmortem-specific altered gray-white matter contrast, laminar cortical intensity heterogeneity, specimen-specific bias fields, and background signal characteristics associated with immersion media: phenomena not typically observed in in vivo data or captured by generic contrast-agnostic synthesis methods. We benchmark our framework against a set of widely used contrast-agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance in high-resolution postmortem infant data. The code is publicly available as part of the purple-mri package.

Coast-to-Coast 2026

Mon, 16 Mar 2026 00:00:00 +0000

Twin Harbors is a partnership between organizations in Houston and Seattle focused on ending sudden, unexplained death. We connect researchers, practitioners, and community leaders to share tools, data, and stories that turn ideas into impact.