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Medical image computing and computer-assisted intervention -- MICCAI 2013 : 16th international conference, Nagoya, Japan, September 22-26, 2013 : proceedings
Computer-Assisted Intervention,健策 森 +1 more
- 01 Jan 2013
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TL;DR: This work presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and expensive process of manually manually cataloging and reconstructing vasculatures and tubular structures in the brain.
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Abstract: Physiological modeling and computer-assisted intervention.- Imaging, reconstruction, and enhancement.- Registration.- Machine learning, statistical modeling, and atlases.- Computer-aided diagnosis and imaging biomarkers.- Intraoperative guidance and robotics.- Microscope, optical imaging, and histology.- Cardiology, vasculatures and tubular structures.- Brain imaging and basic techniques.- Diffusion MRI.- Brain segmentation and atlases.
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Citations
Nuclei graph local features for basal cell carcinoma classification in whole slide images
David Romo-Bucheli,Germán Corredor,Juan D. García-Arteaga,Viviana Arias,Eduardo Romero +4 more
- 26 Jan 2017
TL;DR: Results show that graph topological features extracted from a nuclei based distance graph, particularly those related to local density, have a high predictive value in the automated detection of basal cell carcinoma.
7
A Probabilistic, Non-parametric Framework for Inter-modality Label Fusion
Juan Eugenio Iglesias,Mert R. Sabuncu,Koen Van Leemput +2 more
- 22 Sep 2013
TL;DR: This work proposes a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment and outperforms majority voting and a recently published inter-modality label fusion algorithm.
A sparse representation of the pathologist's interaction with whole slide images to improve the assigned relevance of regions of interest
Daniel Santiago,Germán Corredor,Eduardo Romero +2 more
- 17 Nov 2017
TL;DR: A new method for detecting relevant regions in WSI using the routine navigations in a virtual microscope and determines the hidden relevance by maximizing the incoherence between several paths is contributed.
3
References
Nuclei graph local features for basal cell carcinoma classification in whole slide images
David Romo-Bucheli,Germán Corredor,Juan D. García-Arteaga,Viviana Arias,Eduardo Romero +4 more
- 26 Jan 2017
TL;DR: Results show that graph topological features extracted from a nuclei based distance graph, particularly those related to local density, have a high predictive value in the automated detection of basal cell carcinoma.
7
A Probabilistic, Non-parametric Framework for Inter-modality Label Fusion
Juan Eugenio Iglesias,Mert R. Sabuncu,Koen Van Leemput +2 more
- 22 Sep 2013
TL;DR: This work proposes a label fusion scheme that does not require voxel intensity consistency between the atlases and the target image to segment and outperforms majority voting and a recently published inter-modality label fusion algorithm.
A sparse representation of the pathologist's interaction with whole slide images to improve the assigned relevance of regions of interest
Daniel Santiago,Germán Corredor,Eduardo Romero +2 more
- 17 Nov 2017
TL;DR: A new method for detecting relevant regions in WSI using the routine navigations in a virtual microscope and determines the hidden relevance by maximizing the incoherence between several paths is contributed.
3