Daniel Haehn
Daniel Haehn
Verified email at - Homepage
Cited by
Cited by
Regional infant brain development: an MRI-based morphometric analysis in 3 to 13 month olds
M Choe, S Ortiz-Mantilla, N Makris, M Gregas, J Bacic, D Haehn, ...
Cerebral Cortex 23 (9), 2100-2117, 2013
KJ Gorgolewski, O Esteban, CJ Markiewicz, E Ziegler, DG Ellis, MP Notter, ...
Software, 2018
Design and evaluation of interactive proofreading tools for connectomics
D Haehn, S Knowles-Barley, M Roberts, J Beyer, N Kasthuri, JW Lichtman, ...
IEEE transactions on visualization and computer graphics 20 (12), 2466-2475, 2014
Neuroimaging in the Browser using the X Toolkit
D Haehn, N Rannou, B Ahtam, E Grant, R Pienaar
Frontiers in Neuroinformatics, 2012
Evaluating ‘graphical perception’with CNNs
D Haehn, J Tompkin, H Pfister
IEEE transactions on visualization and computer graphics 25 (1), 641-650, 2018
Fast mitochondria detection for connectomics
V Casser, K Kang, H Pfister, D Haehn
Medical Imaging with Deep Learning, 111-120, 2020
Altered structural brain networks in tuberous sclerosis complex
K Im, B Ahtam, D Haehn, JM Peters, SK Warfield, M Sahin, P Ellen Grant
Cerebral cortex 26 (5), 2046-2058, 2016
Neuroblocks–visual tracking of segmentation and proofreading for large connectomics projects
AK Ai-Awami, J Beyer, D Haehn, N Kasthuri, JW Lichtman, H Pfister, ...
IEEE transactions on visualization and computer graphics 22 (1), 738-746, 2015
Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
F Lekschas, B Peterson, D Haehn, E Ma, N Gehlenborg, H Pfister
Computer Graphics Forum 39 (3), 167-179, 2020
Guided Proofreading of Automatic Segmentations for Connectomics
D Haehn, V Kaynig, J Tompkin, JW Lichtman, H Pfister
IEEE Computer Vision and Pattern Recognition (CVPR), 2017
Scalable interactive visualization for connectomics
D Haehn, J Hoffer, B Matejek, A Suissa-Peleg, AK Al-Awami, L Kamentsky, ...
Informatics 4 (3), 29, 2017
Biologically-constrained graphs for global connectomics reconstruction
B Matejek, D Haehn, H Zhu, D Wei, T Parag, H Pfister
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
Automatic neural reconstruction from petavoxel of electron microscopy data
A Suissa-Peleg, D Haehn, S Knowles-Barley, V Kaynig, TR Jones, ...
Microscopy and Microanalysis 22 (S3), 536-537, 2016
Slice: drop: collaborative medical imaging in the browser
D Haehn
ACM SIGGRAPH 2013 computer animation festival, 1-1, 2013
How machine learning is powering neuroimaging to improve brain health
NM Singh, JB Harrod, S Subramanian, M Robinson, K Chang, ...
Neuroinformatics 20 (4), 943-964, 2022
Imaging a 1 mm3Volume of Rat Cortex Using a MultiBeam SEM
R Schalek, D Lee, N Kasthuri, A Peleg, T Jones, V Kaynig, D Haehn, ...
Microscopy and Microanalysis 22 (S3), 582-583, 2016
ChRIS-A web-based neuroimaging and informatics system for collecting, organizing, processing, visualizing and sharing of medical data
R Pienaar, N Rannou, J Bernal, D Hähn, PE Grant
2015 37th Annual International Conference of the IEEE Engineering in …, 2015
Compresso: Efficient compression of segmentation data for connectomics
B Matejek, D Haehn, F Lekschas, M Mitzenmacher, H Pfister
Medical Image Computing and Computer Assisted Intervention− MICCAI 2017 …, 2017
Two stream active query suggestion for active learning in connectomics
Z Lin, D Wei, WD Jang, S Zhou, X Chen, X Wang, R Schalek, D Berger, ...
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13. 1. Zenodo
KJ Gorgolewski, O Esteban, DG Ellis, MP Notter, E Ziegler, H Johnson, ...
The system can't perform the operation now. Try again later.
Articles 1–20