14 citations · 54 across the 10 of their papers we have counts for
10 papers
PatchMorph: A Stochastic Deep Learning Approach for Unsupervised 3D Brain Image Registration with Small Patches
Henrik Skibbe, Michal Byra, Akiya Watakabe +2
We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of…
Implicit neural representations for joint decomposition and registration of gene expression images in the marmoset brain
Michal Byra, Charissa Poon, Tomomi Shimogori +1
We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anat…
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models
Michal Byra, Muhammad Febrian Rachmadi, Henrik Skibbe
In this work, we investigate the usefulness of vision-language models (VLMs) and large language models for binary few-shot classification of medical images. We utilize the GPT-4 mo…
Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function
Muhammad Febrian Rachmadi, Charissa Poon, Henrik Skibbe
In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addres…
An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain
Charissa Poon, Muhammad Febrian Rachmadi, Michal Byra +4
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists…
Deep Neural Patchworks: Coping with Large Segmentation Tasks
Marco Reisert, Maximilian Russe, Samer Elsheikh +2
Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in parti…
Optimization and Validation of Diffusion MRI-based Fiber Tracking with Neural Tracer Data as a Reference
Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae +11
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network…
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
Henrik Skibbe, Akiya Watakabe, Ken Nakae +10
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on…
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
Marco Reisert, Volker A. Coenen, Christoph Kaller +2
In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract…
Left-Invariant Diffusion on the Motion Group in terms of the Irreducible Representations of SO(3)
Marco Reisert, Henrik Skibbe
In this work we study the formulation of convection/diffusion equations on the 3D motion group SE(3) in terms of the irreducible representations of SO(3). Therefore, the left-invar…