145 citations · 258 across the 10 of their papers we have counts for
5 papers · 1 filter
SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Sven Koitka, Giulia Baldini, Cynthia S. Schmidt +11
Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softma…
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
Johannes Rückert, Louise Bloch, Raphael Brüngel +11
Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper intro…
Open-Source Skull Reconstruction with MONAI
Jianning Li, André Ferreira, Behrus Puladi +7
We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribu…
k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala +5
Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Me…
Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks
Sven Koitka, Lennard Kroll, Eugen Malamutmann +2
Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medi…