no code implementations • 12 Dec 2023 • Henrik Skibbe, Michal Byra, Akiya Watakabe, Tetsuo Yamamori, Marco Reisert
We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration.
1 code implementation • 8 Aug 2023 • Michal Byra, Charissa Poon, Tomomi Shimogori, Henrik Skibbe
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 anatomical structures, but where one image contains additional features or artifacts that are not present in the other image.
no code implementations • 8 Aug 2023 • Michal Byra, Muhammad Febrian Rachmadi, Henrik Skibbe
Moreover, we assess the ability of VLMs to evaluate shape features in breast mass ultrasound images.
1 code implementation • 13 Apr 2023 • 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 addresses the instance imbalance problem commonly encountered when using pixel-wise loss functions such as the Dice loss.
1 code implementation • 13 Mar 2023 • Charissa Poon, Muhammad Febrian Rachmadi, Michal Byra, Matthias Schlachter, Binbin Xu, Tomomi Shimogori, Henrik Skibbe
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.
no code implementations • 7 Jun 2022 • Marco Reisert, Maximilian Russe, Samer Elsheikh, Elias Kellner, Henrik Skibbe
Convolutional neural networks are the way to solve arbitrary image segmentation tasks.
no code implementations • 2 Aug 2019 • Henrik Skibbe, Akiya Watakabe, Ken Nakae, Carlos Enrique Gutierrez, Hiromichi Tsukada, Junichi Hata, Takashi Kawase, Rui Gong, Alexander Woodward, Kenji Doya, Hideyuki Okano, Tetsuo Yamamori, Shin Ishii
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information.
no code implementations • 3 Jul 2018 • Marco Reisert, Volker A. Coenen, Christoph Kaller, Karl Egger, Henrik Skibbe
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 direction and tract presence.