Steffen Bollmann

Nipoppy standardizes neuroimaging data workflows to enable reproducible and comparable multi-center data sharing.

The paper develops a multi-model consensus deep learning approach for automated gold fiducial marker segmentation in prostate MRI, achieving high sensitivity and precision.

This paper introduces a deep learning tool to predict functional topography of the human visual cortex from anatomy, demonstrating its utility across diverse conditions and large-scale datasets.

The paper identifies MRI-derived markers in the VTA linked to depression diagnosis and symptom severity, showing distinct patterns of neuroinflammation and microstructural changes.

VesselBoost is a Python tool for segmenting small blood vessels in human MRA data using deep learning and data augmentation to overcome labeling challenges.

Neurodesk provides a portable and reproducible neuroimaging analysis platform using software containers to enable accessible and flexible data analysis pipelines.

This paper develops a deep learning model using QSM to differentiate gold fiducial markers from calcification in prostate cancer patients, achieving higher accuracy than conventional MRI methods.

This study shows significant individual variability in visual field maps of human V2 and V3, challenging the canonical model of their symmetric organization.

QSMxT improves QSM processing by reducing artefacts and requiring minimal parameter tuning through a robust masking and two-pass reconstruction method.

This paper develops a geometric deep learning model to predict retinotopic organization in human visual cortex from anatomical data, showing improved individual variation prediction.

LASHiS improves longitudinal hippocampal subfield segmentation using multi-contrast MRI, showing better reliability and performance than existing methods.

This paper improves hippocampus subfield segmentation by using non-linear realignment to reduce movement artefacts and enhance image quality in MR scans.

DeepQSM uses deep learning to solve the dipole inversion in MRI for susceptibility mapping, improving the identification of brain substructures and their magnetic properties in diseases like multiple sclerosis.

SHARQnet improves background field removal in QSM by using a deep convolutional neural network, outperforming current methods without parameter tuning.

This paper develops a contrast matching algorithm to align MDA models of the human hippocampus with different contrasts, creating a high-resolution in vivo model for improved segmentation and disease detection.