C
Christine Kroll
Researcher at Technische Universität München
Publications - 5
Citations - 614
Christine Kroll is an academic researcher from Technische Universität München. The author has contributed to research in topics: Segmentation & Convolutional neural network. The author has an hindex of 4, co-authored 5 publications receiving 468 citations.
Papers
More filters
Journal ArticleDOI
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
Fausto Milletari,Seyed-Ahmad Ahmadi,Christine Kroll,Annika Plate,Verena E. Rozanski,Juliana Maiostre,Johannes Levin,Olaf Dietrich,Birgit Ertl-Wagner,Kai Bötzel,Nassir Navab +10 more
TL;DR: A novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs) based on Hough voting, which is robust, multi-region, flexible and can be easily adapted to different modalities is proposed.
Posted Content
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
Fausto Milletari,Seyed-Ahmad Ahmadi,Christine Kroll,Annika Plate,Verena E. Rozanski,Juliana Maiostre,Johannes Levin,Olaf Dietrich,Birgit Ertl-Wagner,Kai Bötzel,Nassir Navab +10 more
TL;DR: In this paper, the authors proposed a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs) based on Hough voting, a strategy that allows for fully automatic localisation and segmentation of the anatomies of interest.
Journal ArticleDOI
Multimodal image-guided prostate fusion biopsy based on automatic deformable registration
Oliver Zettinig,Amit Shah,Christoph Hennersperger,Matthias Eiber,Christine Kroll,Hubert Kübler,Tobias Maurer,Fausto Milletari,Julia Rackerseder,Christian Schulte zu Berge,Enno Storz,Benjamin Frisch,Nassir Navab,Nassir Navab +13 more
TL;DR: A multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance is proposed, able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.
Book ChapterDOI
Robust Segmentation of Various Anatomies in 3D Ultrasound Using Hough Forests and Learned Data Representations
Fausto Milletari,Seyed-Ahmad Ahmadi,Christine Kroll,Christoph Hennersperger,Federico Tombari,Amit Shah,Annika Plate,Kai Boetzel,Nassir Navab,Nassir Navab +9 more
TL;DR: This work proposes a highly adaptive learning-based method for fully automatic segmentation of ultrasound volumes that can robustly and automatically adapt to different anatomies yet enforcing appearance and shape constraints.
Book ChapterDOI
Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
TL;DR: This paper analyses the applicability and performance of Convolutional Neural Networks to localise and segment anatomical structures in medical volumes under clinically realistic constraints: small amount of available training data, need of a short processing time and limited computational resources.